The evolution of natural language processing has been revolutionized by the introduction of the Transformer architecture, which utilizes self-attention mechanisms to capture long-range dependencies in textual data [18]. Modern LLMs, such as GPT [2] and LLaMA [3], scale the architecture to billions of parameters, training on massive corpora to develop emergent abilities in reasoning, coding, and general knowledge generation [19]. LLMs typically require two-phase training: pre-training to learn statistical language patterns, followed by fine-tuning (or instruction tuning) to align the model with specific tasks or human preferences [20]. While pre-training establishes the model’s knowledge base, it requires immense computational resources, often limiting it to centralized data centers equipped with high-performance GPU clusters [21]. Consequently, adapting LLMs to private, domain-specific data remains a challenge, as transferring sensitive information to a central server for fine-tuning often violates data sovereignty regulations.

FL addresses the constraints of data isolation by enabling collaborative model training without exchanging raw data. In the most classical FedAvg algorithm [22], a central server coordinates a global model and distribute it selected clients. Each participating client k performs local training using its local private dataset with the objective of minimizing a local loss function Fk(w), producing a local update. The server then aggregates the clients’ updates, typically via a weighted average, to update the global model parameters w. Mathematically, the objective is to minimize the global loss function F(w):(1)minwF(w)=∑k=1KpkFk(w)where K is the total number of clients and pk represents the relative weight of the k-th client, often proportional to the size of its local dataset. The FL paradigm ensures that raw data never leaves the local device, significantly reducing privacy risks. However, traditional FL faces hurdles such as statistical heterogeneity (Non-IID data), where the data distribution across clients varies significantly, leading to model drift and slow convergence [23].

Fig. 3 depicts the general pipeline of fine-tuning and personalization of LLMs in a federated setting. Federated LLMs extend the FL paradigm to the training and fine-tuning of transformer-based architectures. Unlike traditional FL, which often trains models from scratch, federated LLMs typically focus on federated fine-tuning of pre-trained LLMs. Specifically, the global model is initialized with pre-trained weights. Clients collaborate to adjust the weights (or a subset thereof) based on private instruction sets or domain-specific corpora.

The integration of LLMs into federated settings introduces unique system challenges not present in standard FL. The sheer size of LLM parameters, ranging from billions to trillions, imposes severe communication bottlenecks when transmitting massive model updates between clients and the central server. In addition, the memory requirements for backpropagation often exceed the hardware capabilities of edge devices or consumer-grade GPUs found in decentralized nodes. Consequently, the standard FedAvg approach is often computationally infeasible for LLMs, necessitating the adoption of parameter-efficient techniques and communication-compression strategies to make distributed training viable.

In federated LLMs, heterogeneity arises along at least three distinct dimensions: data, system, and tasks. Data heterogeneity refers to the discrepancies in local data distributions, label spaces, and data quality across clients, which often induce client drift and unstable convergence. System heterogeneity refers to varieties in hardware capabilities, memory, bandwidth, and availability, leading to stragglers, stale updates, and unequal participation. Task heterogeneity refers to differences in downstream tasks, personalization targets, or alignment preferences across clients, which may render a single global optimum ill-defined and lead to negative transfer under naive aggregation.

The prohibitive memory requirements of LLMs often render standard FL infeasible on edge devices with limited resources. Split FL addresses the bottleneck by partitioning the model architecture between the client and server. In split FL, the client executes the initial layers (the head) to generate intermediate activations, which are then transmitted to the server for forward propagation through the remaining layers (the tail) [24]. Gradients flow in reverse during backpropagation. The structural division allows clients to train massive models while holding only a fraction of the parameters locally.

Implementing split FL for LLMs requires robust frameworks that can handle the high communication overhead of transmitting activation maps. FedsLLM [25] introduces a parallelized split FL architecture specifically optimized for communication networks. By enabling parallel training across multiple clients and synchronizing the split-layer boundaries, the framework mitigates the straggler effect common in sequential split learning. FedsLLM significantly reduces the computational load on edge devices compared to full-model FL, although it introduces a heavy dependency on uplink bandwidth.

VFLAIR-LLM [26] is proposed to rigorously evaluate different split federated LLM architectures. It provides a comprehensive benchmark suite. VFLAIR-LLM elucidates the trade-offs between cut-layer selection, communication latency, and model performance, and serves as a critical tool for system designers, offering metrics that quantify how different splitting strategies affect the convergence rate and resource consumption of various LLM backbones.

While architectural splitting solves memory constraints, it introduces optimization challenges, particularly when data distributions across clients are non-IID. The separation of layers can decouple the learning of low-level features (client-side) from high-level semantic reasoning (server-side).

To counter potential degradation in generalization capability, recent research has integrated advanced minimization techniques into the split FL workflow. Tan et al. [27] propose incorporating sharpness-aware minimization into the local client updates. By seeking parameters that lie in neighborhoods of uniformly low loss rather than sharp minima, the method improves the model’s robustness to heterogeneous data. The integration ensures that the split configuration does not compromise the global model’s ability to generalize across diverse user prompts.

Split FL is frequently cited as a privacy-preserving solution because raw data remains local. However, the transmission of intermediate activations creates a new attack surface. A critical analysis [28] challenges the assumption of inherent security. In particular, inversion attacks can reconstruct original inputs from cut-layer activations, especially in the context of LLMs with high semantic density. The finding necessitates the integration of differential privacy or activation compression mechanisms to obfuscate the transmitted signals.

Furthermore, the reliance on continuous communication makes split FL vulnerable to physical-layer disruptions. R-SFLLM [29] addresses the issue by proposing a jamming-resilient framework. Recognizing that wireless channels are susceptible to interference, the authors design a robust transmission protocol that maintains training stability even under active jamming attacks. R-SFLLM ensures that the collaborative fine-tuning process remains viable in hostile or unstable network environments.

The deployment of LLM within federated settings necessitates a paradigm shift from simple parameter aggregation to complex edge-cloud orchestration. Unlike traditional FL, where models are small enough for trivial on-device processing, LLMs impose severe computational and memory demands. Consequently, recent literature focuses on intelligent scheduling, resource-aware offloading, and collaborative architectures that bridge the gap between high-capacity cloud servers and resource-constrained edge devices.

Standard FedAvg suffers from the straggler effect, where the global training speed is bottlenecked by the slowest device. The straggler issue is exacerbated in federated LLM due to the heterogeneity of edge hardware. To address the issue, Tri-AFLLM [30] introduces a resource-efficient adaptive framework. By abandoning synchronous lock-step updates in favor of an asynchronous protocol, the system allows faster clients to contribute more frequently while slower nodes update partially. Tri-AFLLM significantly improves convergence speed without idling powerful resources.

Beyond timing, the qualitative match between a client’s data and the model’s objective is crucial. FedCLLM [31] shows that random client selection is inefficient for LLM fine-tuning. Instead, it utilizes domain descriptions to match clients with specific downstream tasks. By filtering participants based on the semantic relevance of local data, the framework ensures that the global model aggregates high-value updates, reducing communication rounds and improving task-specific performance.

Once participants are selected, orchestrating the compute resources becomes important. LTQA [32] proposes a delay-optimization strategy. It continuously monitors network latency and computational throughput, dynamically assigning training loads to nodes that minimize the overall system delay.

Software optimization alone is often inadequate to bridge the resource gap for LLMs. Recent approaches have begun to exploit specialized hardware at the edge. Huang et al. [33] integrate Embedded Data Processing Units (DPUs) into the workflow. By offloading specific tensor operations and data preprocessing tasks to DPUs, the main CPU/GPU is freed for core model updates, effectively expanding the compute envelope of edge devices.

For environments where individual devices cannot hold even a quantized model, DisLLM [34] proposes a distributed inference and training architecture. The framework partitions the LLM across a mesh of resource-constrained devices, treating the edge network as a single cohesive computer. While DisLLM enables deploying larger models, it requires rigorous privacy assurances to prevent data leakage between collaborating nodes.

To unify the methods above, comprehensive frameworks are required. MPCTF [35] establishes a multi-party collaborative training protocol that standardizes the interaction between data owners, compute providers, and model architects. The abstraction layer simplifies the setup of decentralized LLM training.

Complementing the training phase, FoRA [36] focuses on efficiently propagating knowledge from the cloud to the edge. It optimizes the fine-tuning process for on-device LLMs by selectively transferring parameters that yield the highest accuracy gains, thereby minimizing bandwidth consumption during the downlink phase. Meanwhile, ensuring the federated LLM systems operate securely in production can be addressed by federated data modeling [37], which outlines cloud-native architectures for deploying collaborative models while adhering to strict security compliance standards.

Centralized orchestration in federated LLMs often suffers from a single point of failure and limited scalability. Furthermore, the dependence on a central server raises concerns regarding censorship and trust. To mitigate the risks, the architecture of federated LLM is increasingly moving towards decentralized, peer-to-peer topologies, often underpinned by blockchain technology to ensure integrity and incentivize participation.

In environments lacking a trusted central authority, blockchain ledgers provide an immutable record of model updates. PureLLM [38] introduces a blockchain-driven decentralized framework specifically for personalized FL. By removing the central aggregator, PureLLM allows devices to exchange updates directly via a peer-to-peer mesh. The blockchain consensus mechanism validates these updates, filtering out malicious contributions before they are assimilated into the local models. The structure not only enhances robustness against poisoning attacks but also improves resource efficiency by allowing nodes to selectively assimilate knowledge relevant to specific tasks.

A critical challenge in decentralized federated LLM is the free-rider problem, where participants obtains the global model with limited or no contributions. DISM [39] addresses the free-rider problem by embedding a reward system within the blockchain protocols. Smart contracts automatically dispense tokens or reputation marks to clients based on the quality and volume of data contributions. Such an economic layer is essential for sustaining long-term collaborative fine-tuning, because the computational cost of training LLMs is too high for altruistic participation alone.

The utility of federated LLMs relies heavily on the quality of distributed data and how the data is used. Unlike centralized training, where data is curated and shuffled, federated environments suffer from extreme statistical heterogeneity (Non-IID data) and varying data quality. Furthermore, the static nature of parametric knowledge in LLMs conflicts with the dynamic information available at the edge. Hence, we explore strategies to mitigate data heterogeneity through advanced filtering and distillation, as well as methods to augment generation using distributed knowledge bases.

Federated LLMs faces a critical bottleneck: the prohibitive cost of full-parameter fine-tuning. With model sizes ranging from billions to trillions of parameters, transmitting full gradient updates saturates communication bandwidth and overwhelms the memory capacities of edge devices. Federated PEFT avoids transmitting full gradient updates by freezing the pre-trained backbone and updating only a small subset of parameters or additional adapter modules [46]. Among different strategies, Low-Rank Adaptation (LoRA) [47] is regarded as the de facto standard, enabling clients to train low-rank matrices that approximate weight updates. Recent literature has expanded beyond basic LoRA implementations to address specific federated challenges, including system heterogeneity, communication constraints, and data personalization.

Federated PEFT reduces the computational burden by updating a subset of model parameters; however, it still requires access to the model’s gradients and internal weights. Such a requirement poses a barrier for clients with severe resource constraints or when the LLM is deployed as a black-box service. Federated prompt engineering addresses these limitations by optimizing the input space rather than the model space [59]. By learning optimal discrete tokens or continuous embeddings, federated prompt engineering enables clients to steer the global model’s behavior with minimal communication overhead.

Training LLMs within a federated ecosystem imposes severe memory and communication constraints [65]. Standard backpropagation is often infeasible on consumer-grade edge devices because it requires storing activation maps and optimizer states. Consequently, the research community has pivoted toward advanced optimization paradigms that circumvent full-precision gradient computation. The techniques include Zeroth-Order optimization, heterogeneous quantization, and hybrid gradient strategies.

While FL fundamentally mitigates privacy risks by retaining raw data on local devices, integrating LLMs introduces novel vulnerabilities. The vast parameter space of LLMs allows for the unintended memorization of training data [72], and the exchange of high-dimensional gradients or parameter updates can be exploited to reconstruct original inputs [73]. Consequently, privacy mechanisms in federated LLM must evolve beyond standard aggregation to address specific threats ranging from sensitive data identification to cross-cloud leakage.

Effective privacy preservation begins before the training process initiates. Traditional FL assumes that keeping data local is sufficient, yet it overlooks the risk that models may inadvertently learn and regurgitate personally identifiable information. FedAPILLM [74] proposes utilizing the federated LLM framework itself to detect vulnerabilities. By training a federated model to recognize sensitive fields within API structures, the system can automatically flag or redact such information in real time. FedAPILLM proactively ensures that the data fed into the fine-tuning process is sanitized, thereby reducing the surface area for potential privacy breaches during subsequent model interactions.

The gradients exchanged during LLM fine-tuning contain significant information about the local batch data. Adversaries can employ gradient inversion techniques to reconstruct the original text. A recent study [75] highlights that the risk is particularly acute in LLMs due to the semantic richness of the text data. The authors advocate advanced noise injection mechanisms that go beyond standard Differential Privacy (DP) to address gradient inversion attacks. By analyzing the correlation between gradient sparsity and information leakage, the proposed methods selectively perturb updates to maximize privacy while preserving linguistic quality.

Applying heavy cryptographic protocols or uniform noise to billions of parameters is computationally prohibitive and detrimental to model convergence. Pan and Wu [76] address the efficiency bottleneck. The core premise is that not all model parameters contribute equally to privacy leakage. By identifying and encrypting only the most sensitive subsets of parameters (often those associated with rare tokens or specific attention heads), the system significantly reduces computational overhead while maintaining robust protection levels.

Besides selective encryption, sampling strategies offer a statistical shield. FCLM [77] introduces a method designed for non-IID environments. Instead of aggregating updates from all clients or random subsets, the system clusters clients based on similarities in their data distributions. By sampling from the clusters, the aggregation process masks the contribution of any single device within the group variance. FCLM not only enhances privacy by breaking the direct lineage between a specific client and the global update but also stabilizes training on heterogeneous data.

While privacy mechanisms protect data confidentiality, they do not inherently secure the model against integrity attacks. The distributed nature of FL introduces significant attack surfaces, particularly Model Poisoning and Backdoor Attacks, where malicious clients inject deceptive updates to manipulate the global model’s behavior [78]. In the context of LLMs, the threats are amplified by the model’s vast parameter space and the opacity of deep neural networks. Furthermore, the deployment of federated LLM in wireless and edge environments necessitates robustness against not only malicious actors but also adversarial environmental conditions.

Backdoor attacks in FL typically involve a sophisticated adversary embedding a hidden trigger into the model, causing it to misclassify inputs only when the trigger is present [79]. In the era of LLMs, backdoor attacks have become more complex. The work LBKD [80] highlights a critical vulnerability in specialized domains. Specifically, standard aggregation is insufficient to filter out subtle triggers embedded in domain-specific data. The proposed bidirectional knowledge distillation framework reveals a dual nature: while distillation is often used for model compression, it can also serve as a sophisticated vector for implanting persistent backdoors that survive aggregation. It suggests that the very mechanisms used to make federated LLM efficient require rigorous security auditing to prevent the propagation of malicious traits.

Beyond malicious data injection, the physical transmission medium presents a security challenge. Federated LLM deployments often rely on wireless channels susceptible to noise and intentional jamming. ROFED-LLM [81] addresses the fragility of LLM training in adversarial wireless environments. The study shows that standard robust aggregation algorithms focus primarily on outlier updates caused by data poisoning but fail to account for channel-induced corruption. By modeling the adversarial interference in the wireless spectrum, ROFED-LLM introduces a channel-aware aggregation scheme. It ensures that the global model maintains high fidelity even when the communication links are actively compromised or heavily degraded, a prerequisite for deploying federated LLM in critical infrastructure.

Interestingly, recent research shifts the paradigm from protecting the model to using the model as a protection mechanism, as LLMs possess strong reasoning capabilities that can be harnessed to secure underlying systems.

FedITD [82] exemplifies the idea by applying PEFT to the domain of Insider Threat Detection. Traditional centralized detection systems risk exposing sensitive user logs. FedITD utilizes pre-trained LLMs to analyze behavioral logs locally. By fine-tuning the model via FL, the system learns to identify complex, non-linear patterns of insider threats across an organization without centralizing the raw audit trails. It demonstrates that federated LLM can serve as a potent cybersecurity tool, provided the training process itself remains secure.

Similarly, Luo and Ji [83] propose a framework to enhance the security of Edge-Cloud AI systems. LLMs are employed to monitor data collaboration flows between edge devices and the cloud. The LLM acts as a semantic guardian, identifying anomalous data exchanges that deviate from established security protocols. It creates a symbiotic relationship where the FL framework updates the security model, and the security model, in turn, protects the FL infrastructure.

Ensuring that LLMs align with human intent and ethical standards is essential. In centralized settings, alignment and fairness are typically achieved through Reinforcement Learning from Human Feedback (RLHF) [20]. However, the federated paradigm introduces unique challenges: human preferences are heterogeneous across clients, and sensitive demographic data required for fairness auditing remains local. Hence, decentralized alignment strategies have been popular to balance global convergence with pluralistic values and rigorous fairness guarantees.

The direct translation of RLHF to federated environments is non-trivial due to the communication overhead of maintaining multiple models (actor, critic, reward, and reference models). FedRLHF [84] addresses the structural impediments by proposing a framework that ensures convergence with privacy preservation. Theoretical guarantees are provided that federated policy optimization can match centralized performance, assuming a coherent global preference exists.

However, the assumption of a single global preference is often flawed. Different cultures and user groups possess distinct values. PluralLLM [85] challenges the one-size-fits-all alignment paradigm. Instead of aggregating conflicting feedback into a diluted global average, the framework facilitates pluralistic alignment. By leveraging the natural data partitioning of FL, PluralLLM allows the model to maintain multiple alignment heads or adapt to diverse value systems, thereby respecting the heterogeneity of the user base rather than suppressing it.

In the literature, Proximal Policy Optimization (PPO) is often considered the standard for RLHF; however, it is computationally intensive on edge devices. Direct Preference Optimization (DPO) [86] has emerged as a resource-efficient alternative. Recent work explores the intersection of DPO and behavioral economics in federated LLMs. KTO [87] investigates how human cognitive biases, specifically loss aversion defined in Prospect Theory, influence federated fine-tuning. The authors argue that standard DPO assumes rational preference labeling. By modeling the non-linear value perception of human annotators (where losses loom larger than gains), the proposed method refines the loss function to better capture true user intent, leading to more robust alignment in distributed settings.

Besides alignment, fairness is also important in LLMs. Fairness in federated LLM is two-fold: ensuring the model does not discriminate based on protected attributes, and ensuring fair representation of client contributions.

In the context of prompt engineering, bias often propagates from the input phrasing. FedPSF-LLM [88] introduces a mechanism to vet prompts locally before the prompts influence the global model. By evaluating the response disparities across demographic groups at the client level, the system filters out prompts that trigger discriminatory outputs, preventing bias from polluting the global aggregation.

Specific domains require even stricter adherence to fairness and privacy. In the Internet of Medical Things, an incorrect or biased recommendation can have life-altering consequences. PFFPO [89] integrates differential privacy with fairness constraints directly into the optimization objective. The method employs a multi-objective approach that maximizes helpfulness while simultaneously minimizing the statistical distance between outputs across different patient demographics. It ensures that the alignment process does not inadvertently favor specific medical profiles over others.

The evaluation of security, privacy, and fairness mechanisms in federated LLMs depends critically on the assumed adversary model. Relevant dimensions include whether the server is honest-but-curious or malicious, whether attackers control one or multiple colluding clients, and whether the attack surface lies in model updates, communication messages, outputs, or retrieval components. Accordingly, reported defense performance should be interpreted together with the attacker’s capabilities and prior knowledge.

In practice, the defense mechanisms are commonly evaluated along several complementary dimensions: attack success or backdoor persistence for integrity attacks; exposure risk under membership, reconstruction, or attribute inference for privacy attacks; utility degradation in downstream task performance; and system overhead in communication, latency, or computation. We therefore emphasize that rigorous comparison requires reporting not only defense effectiveness but also the associated privacy-utility-efficiency trade-offs under clearly stated threat assumptions.

The biomedical domain is among the most critical yet challenging environments for deploying LLMs. While general-purpose LLMs demonstrate remarkable capabilities in natural language understanding, the direct application in healthcare is impeded by strict privacy regulations, such as GDPR [90], and the siloed nature of medical records. FL has consequently emerged as a vital paradigm, enabling the training of robust biomedical models across distributed institutions without the need for centralized data aggregation. Recent surveys highlight that integrating LLMs into federated healthcare networks offers unique opportunities to address data heterogeneity and scarcity, though significant challenges remain regarding interpretability and communication overhead [91].

One of the most actively explored applications of federated LLMs in healthcare is the extraction of insights from unstructured clinical text, such as physician notes and discharge summaries, although current evidence is still concentrated in prototype studies and controlled evaluations rather than routine clinical deployment. Pharmacovigilance, specifically the identification of Adverse Drug Reactions (ADR), benefits significantly from federated LLMs. By leveraging federated architectures, researchers can aggregate knowledge from diverse patient populations to identify rare side effects without compromising patient anonymity [92]. However, the computational cost of fine-tuning massive language models on local hospital servers, which often lack high-end GPU clusters, acts as a barrier to adoption. To mitigate the resource constraints, recent methodologies propose selective layer fine-tuning. Instead of updating all model parameters or utilizing standard adapters, there are techniques to identify and train only the most relevant layers of the transformer architecture, thereby achieving performance comparable to full fine-tuning while drastically reducing memory usage and communication costs [93].

Beyond static text analysis, modern healthcare requires predictive modeling that accounts for the longitudinal nature of patient history. Electronic Health Records (EHR) are inherently temporal, containing sequences of visits, diagnoses, and treatments. Standard LLMs often treat input data as static context, failing to capture the dynamic progression of chronic conditions. Novel frameworks now incorporate temporal-aware mechanisms within the federated setting. These approaches utilize time-series capable prompts and specialized attention mechanisms to model disease progression, allowing the global model to learn temporal patterns of patient deterioration across multiple hospitals while keeping the raw temporal sequences local [94].

The scope of federated LLM in medicine extends beyond text to multimodal applications, particularly in medical imaging and report generation. In colonoscopy analysis, precise polyp segmentation is critical for early cancer detection. Integrating vision-language foundation models with federated strategies allows for the development of clinically applicable tools. By employing LoRA within a federated framework, institutions can collaboratively refine large vision models for specific segmentation tasks, ensuring high accuracy and privacy preservation simultaneously [95].

More broadly, multimodal federated LLMs deserve explicit attention because many realistic deployments combine text with images, video, waveforms, or sensor streams rather than relying on text alone. A representative case is medical image-report collaboration, where one institution may hold radiology images while another contributes report corpora or downstream annotation expertise. In such settings, federated multimodal adaptation must address not only standard non-IID effects but also cross-modal representation alignment, missing modalities across clients, and stronger privacy risks from perceptual data. Parameter-efficient adaptation can be particularly attractive because it can localize updates to modality-specific encoders or fusion layers, but aggregating such heterogeneous multimodal adapters remains less mature than in text-only federated LLMs. It suggests that multimodal federated LLM applications are promising but remain constrained by limited benchmarks, evaluation protocols, and theory.

Furthermore, the generative capabilities of LLMs have been explored for administrative and diagnostic documentation tasks such as medical report generation and summarization. Existing studies indicate encouraging performance in controlled settings, but broader validation is still needed before these systems can be considered reliable for routine clinical workflows. Generating medical reports from heterogeneous data sources poses a challenge due to the varying data formats and equipment standards across different hospitals. Communication-efficient heterogeneous FL frameworks have been developed to address the issue. The developed systems allow hospitals with different local model architectures or computational capabilities to collaborate on a shared objective, such as generating coherent radiology reports, by exchanging knowledge through prototype alignment or distilled representations rather than raw gradients [96]. It ensures that even smaller clinics with limited infrastructure can benefit from and contribute to state-of-the-art medical report generation systems.

The integration of LLMs into the Industrial Internet of Things (IIoT) and next-generation networks marks a transition from static sensing to intelligent, semantic reasoning at the edge. A comprehensive review of the synergy suggests that FL is essential for deploying these models in networked systems, primarily to navigate the trade-offs between the computational demands of LLMs and the strict privacy requirements of industrial data [97].

In the realm of autonomous mobility, vehicles act as mobile computing nodes that require real-time decision-making capabilities. Traditional cloud-centric training struggles with the high latency and bandwidth limitations inherent in vehicular networks. To address the issue, recent frameworks like iFLOW have introduced scalable multi-model FL architectures specifically designed for computing on the wheels, enabling cars to collaboratively learn from diverse road conditions without sharing raw sensor streams [98]. Furthermore, the heterogeneity of driving scenarios necessitates robust adaptation techniques. Federated instruction tuning strategies have been proposed to enhance feature diversity, allowing autonomous systems to generalize better across varying traffic environments and rare edge cases [99]. Besides control systems, in-cabin intelligent assistants are also evolving through federated strategies. By applying LoRA to automotive systems, manufacturers can fine-tune LLMs for personalized driver interactions while minimizing the communication overhead associated with transmitting full model updates [100].

Unmanned Aerial Vehicle (UAV) swarms pose distinct challenges, characterized by high mobility and intermittent connectivity. Standard federated protocols often fail when nodes frequently drop out of the network. Specialized algorithms for low-altitude UAV networks now incorporate dropout resilient mechanisms, ensuring that the collaborative fine-tuning of LLMs remains stable even when swarm members disconnect unexpectedly due to interference or battery constraints [101].

Beyond mobility, the manufacturing sector is leveraging federated LLM to enhance the intelligence of digital twins and production lines. Edge-centric architectures are being developed to bring LLM inference closer to the factory floor, addressing the latency requirements of real-time process monitoring [102]. A critical application in manufacturing is the maintenance of Digital Twins, which requires massive amounts of labeled data. Novel asynchronous FL frameworks empower the digital replicas by utilizing LLMs for intelligent data labeling. Such an approach facilitates secure data sharing and model training across different manufacturing sites, effectively bridging the gap between physical assets and the virtual counterparts [103].

The underlying network infrastructure supporting the applications above also benefits from LLMs. As 5G networks become increasingly complex, identifying security threats is critical. FedLLMGuard utilizes federated LLMs to detect anomalies in network traffic, leveraging LLMs’ semantic understanding to identify subtle attack patterns that traditional rule-based systems might miss [104]. Similarly, in video surveillance, FedVAD enhances anomaly detection by employing GPT-driven semantic distillation. The technique transfers the rich semantic knowledge of large models into lightweight edge detectors, improving the identification of unusual events in video streams while preserving privacy [105].

Finally, the application of federated LLM extends to the design of the hardware itself and scientific discovery. In the complex field of chip design, the FedChip framework demonstrates how federated LLMs can assist in generating and optimizing Verilog code for AI accelerators, allowing disparate design houses to collaborate on better chip architectures without revealing proprietary intellectual property [106]. In scientific modeling, adaptive FL with local LLMs has proven effective for simulating complex photonic and chemical systems, accelerating discovery by aggregating insights from distributed experimental data [107].

The application of LLMs to software engineering has revolutionized coding workflows, yet the proprietary nature of commercial source code presents a barrier to centralized training. FL offers a viable path to leverage private code repositories for model improvement without exposing intellectual property. To address the practical constraints of distributed development environments, the F-CodeLLM framework introduces a methodology for adapting LLMs to software tasks across decentralized clients, ensuring that local coding patterns contribute to global model intelligence without raw data leakage [108]. Similarly, adaptive fine-tuning frameworks have been proposed to address the heterogeneity of developer environments, enabling models to dynamically adapt to specific programming languages and project requirements across different organizations [109].

A critical task in software maintenance is understanding legacy code. Recent research explores code summarization techniques that function without direct access to the source text. By leveraging federated strategies, systems can generate natural language descriptions of code functionality while keeping the underlying logic on local servers, thereby maintaining strict confidentiality [110]. Security within the deployment pipeline also benefits from this distributed approach. Novel prompt engineering strategies have been integrated into FL workflows to enhance the detection of malicious code during deployment, ensuring that security checks evolve collaboratively across different nodes to identify emerging threats [111].

Beyond software code, the utility of federated LLMs extends to broader text processing and rule-generation tasks. In the domain of complex event processing (CEP), defining precise rules for event detection is often labor-intensive. Federated LLMs facilitate the automated generation and refinement of CEP rules by aggregating diverse event patterns from multiple sources, thereby improving the system’s ability to interpret complex scenarios [112]. Archival science has also seen the introduction of federated intelligence, in which LLMs assist in generating automated descriptions of archival materials. The application allows institutions to modernize their catalogs collaboratively while respecting the privacy or sensitivity of specific historical records [113].

Specialized text and audio processing domains further demonstrate the versatility of federated LLMs. The FL-former architecture adapts the Transformer model for Chinese automatic speech recognition in a federated setting, addressing data scarcity issues often encountered in dialect- or domain-specific datasets [114]. In the entertainment industry, the FedNPC framework utilizes FL to power Non-Player Characters (NPCs) in games. By training on distributed player interactions, game developers can create more dynamic and responsive character dialogues without centralizing massive logs of player behavior [115]. Furthermore, robust aggregation techniques are being applied to multimodal knowledge discovery in computational social systems, allowing researchers to analyze social trends and text data across disparate platforms while mitigating the noise and reliability issues inherent in user-generated content [116]. Besides, FedJudge reports recent advances in federated LLMs for the legal profession [117].

The financial sector operates under some of the most stringent regulatory frameworks regarding data privacy and security, making the centralization of transaction records or customer profiles nearly impossible. Consequently, federated LLMs are increasingly being explored as a way to unlock the value of financial data while remaining more compatible with privacy and compliance constraints [118]. However, much of the current evidence remains at the prototype system and task-specific experimental validation stages. A primary challenge in finance is the computational cost of deploying massive models on the secure, often resource-constrained infrastructure of smaller banks or local branches.

To bridge the gap between model capability and deployment constraints, researchers have introduced Federated Financial Reasoning Distillation [119]. The approach involves a student-teacher paradigm where a compact financial expert model is trained by distilling reasoning capabilities from multiple larger teacher models distributed across different institutions. It allows the smaller model to inherit complex financial reasoning skills, including risk assessment and market trend analysis, without ever directly accessing the teachers’ private training data. Such strategies democratize access to high-level financial AI, enabling smaller entities to leverage the collective intelligence of the market while maintaining absolute data sovereignty.

Overall, current federated LLM applications in finance are promising for tasks such as risk analysis, reasoning distillation, and panic-index or market-sentiment related analysis, but evidence for deployment in high-stakes decision pipelines remains limited and demands stress testing and regulatory evaluation.