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Personalized Wireless Federated Learning for Large Language Models

Feibo Jiang, Li Dong, Siwei Tu, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Dusit Niyato

arXiv:2404.13238v2cs.LGcs.AIcs.CL

TL;DR

Wireless federated LLM training faces heterogeneous data, device resource constraints, and communication overhead. The paper proposes PWFF, combining parameter-efficient fine-tuning, partial aggregation, and personalized alignment; simulations report the highest instruction-tuning accuracy and improved alignment rewards with lower energy use than PFL.

Abstract

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Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges related to security and privacy. Federated Learning (FL), with its decentralized architecture, offers enhanced data privacy protection. Nevertheless, when integrated with LLMs, FL still struggles with several critical limitations, including large-scale and heterogeneous data, resource-intensive training, and substantial communication overhead. To address these challenges, this paper first presents a systematic analysis of the distinct training stages of LLMs in wireless networks, including pre-training, instruction tuning, and alignment tuning. Building upon this foundation, we propose a Personalized Wireless Federated Fine-tuning (PWFF) framework. Initially, we utilize the adapter and Low-Rank Adaptation (LoRA) techniques to decrease energy consumption, while employing global partial aggregation to reduce communication delay. Subsequently, we develop two reward models and design a personalized loss function to fulfill the goal of personalized learning. Furthermore, we implement a local multi-objective alignment to ensure the stability and effectiveness of the FL process. Finally, we conduct a series of simulations to validate the performance of the proposed PWFF method and provide an in-depth discussion of the open issues.

I. INTRODUCTION

Wireless LLM training must contend with private data and the scale of modern models, while federated learning still faces heterogeneous client data, resource limits, and communication overhead. PWFF combines adapters, LoRA, global partial aggregation, personalized reward models, and local multi-objective alignment for wireless federated fine-tuning.

1) Big and Heterogeneous Data:

  • Client data may be highly unbalanced and diverse, slowing convergence and worsening LLM training performance in wireless federated learning.Differences can reflect clients’ backgrounds, preferences, or behaviors.Source: 1) Big and Heterogeneous Data · p. 1

2) Resource-intensive Training:

  • LLM training demands substantial computation and memory, which can exclude resource-constrained devices or slow training and impair performance.Wireless federated learning distributes computation across participating devices.Source: 2) Resource-intensive Training · p. 1

3) High Communication Overhead:

  • Wireless federated LLM training requires frequent server-device communication, creating bandwidth and latency costs that grow with rounds and participating devices.Reducing this overhead without sacrificing model performance is identified as a challenge.Source: 3) High Communication Overhead · p. 1

I. INTRODUCTION

  • PWFF combines adapters and LoRA with global partial aggregation, personalized reward models, and local multi-objective alignment for wireless LLM fine-tuning.The framework is presented as addressing energy use, communication delay, and personalization.Source: 3) High Communication Overhead · p. 2

II. HARMONIZING LLM AND FL IN WIRELESS NETWORKS

LLMs use pre-training to acquire general language understanding and fine-tuning to adapt it to specific tasks or goals. The section describes server-side pre-training and explains why federated learning may be unnecessary for that stage.

II. HARMONIZING LLM AND FL IN WIRELESS NETWORKS

  • LLMs have two learning stages: pre-training builds general language understanding, while fine-tuning adapts it to specific tasks or goals.Fig. 1 illustrates these stages.Source: §II · p. 2, Figure 1 · p. 2

A. Pre-training Stage of LLM

  • Server-side pre-training uses large volumes of unlabelled data to teach language patterns and knowledge by predicting missing or masked words.The passage gives BERT’s masked language modelling as an example.Source: A. Pre-training Stage of LLM · p. 2
  • Federated pre-training is presented as potentially unnecessary because training all parameters requires substantial resources and distributed updates add communication overhead.Centralized training can use powerful servers and optimized infrastructure more efficiently.Source: A. Pre-training Stage of LLM · p. 2
  • The paper argues that public pre-training corpora generally lack sensitive personal information, whereas federated learning is more applicable to sensitive, decentralized data.The passage contrasts public text corpora with settings such as healthcare and personal mobile devices.Source: A. Pre-training Stage of LLM · p. 2

B. Fine-tuning Stage of LLM

LLM fine-tuning uses smaller, often local datasets and client-side updates; instruction tuning applies supervised learning to improve instruction following across tasks.

B. Fine-tuning Stage of LLM

  • Clients fine-tune on smaller, often private datasets and send updated parameter subsets to the server over wireless networks.The fine-tuning itself occurs locally on the client side to protect local data.Source: B. Fine-tuning Stage of LLM · p. 2
  • Fine-tuning uses less data and computation than pre-training, and its local datasets are often labeled with answers to learn.Source: B. Fine-tuning Stage of LLM · p. 3

1) Instruction Tuning:

  • Instruction tuning uses supervised learning and back-propagation on instruction datasets to improve instruction following and generalization across task types.Source: 1) Instruction Tuning · p. 3

2) Alignment Tuning:

Alignment tuning seeks outputs aligned with human values and safer behavior; the passages motivate federated fine-tuning for privacy-sensitive or decentralized data and describe adapters as a lightweight personalization option.

2) Alignment Tuning:

  • Alignment tuning uses reward mechanisms to strengthen positive behaviors and discourage negative ones, aiming for safer, more reliable outputs aligned with human intentions.Source: 2) Alignment Tuning · p. 3
  • Federated learning is motivated for fine-tuning when data is private or decentralized; fine-tuning is more resource-efficient than pre-training, which is better suited to centralized learning.The paper focuses on federated learning for fine-tuning because pre-training involves massive insensitive datasets and extensive model parameters.Source: 2) Alignment Tuning · p. 3

A. Federated Instruction Tuning

1) Adapter:

  • Adapters insert bottleneck modules between LLM layers and update only those parameters, reducing trainable parameters and local computation and storage overhead.Source: 1) Adapter · p. 3

2) LoRA:

The section describes LoRA as a way to reduce the cost of federated fine-tuning and reviews alignment approaches for handling human feedback and diverse preferences. It also identifies reward design and training stability as challenges for federated alignment.

2) LoRA:

  • LoRA updates smaller low-rank matrices, reducing client communication and computation costs while potentially preventing catastrophic forgetting and preserving generation and generalization abilities [8].The method preserves historical low-rank matrices during fine-tuning.Source: 2) LoRA · p. 3

B. Federated Alignment Tuning

1) Reinforcement Learning from Human Feedback (RLHF):

  • RLHF ranks candidate responses with human annotators, trains a reward model from those rankings, then uses it to guide policy optimization; FL can update only a small parameter subset.The passage also describes adapting RLHF across distributed clients [8].Source: 1) Reinforcement Learning from Human… · p. 3

2) Multi-objective preference alignment:

  • Multi-objective alignment can combine weighted rewards to represent differences in usefulness, harmlessness, and safety preferences, but federated training is complex and unstable [10].The passage calls for specialized robust learning methods.Source: 2) Multi-objective preference alignment · p. 3

A. Current Research Progress

Prior wireless FL–LLM work targets unified models, while PFL offers a way to adapt learning to individual clients. Its described advantages span personalized data, local models, and communication.

A. Current Research Progress

B. Advantages of Personalized Federated Fine-tuning.

1) Personalized User Data:

2) Customized Local Model:

  • A personalized local LLM lets tuning adapt to each device’s preferences and computational, storage, and communication constraints.Source: 2) Customized Local Model · p. 4

3) Specific Communication Process:

C. Key Innovations of PWFF

PWFF personalizes LLM fine-tuning around clients’ helpfulness and harmlessness preferences. It combines partial aggregation, separate reward models, and local multi-objective alignment in a federated workflow.

C. Key Innovations of PWFF

  • PWFF lets each client set personalized requirements for LLM helpfulness and harmlessness.Source: C. Key Innovations of PWFF · p. 4
  • Only adapter parameters are globally aggregated; local LoRA parameters remain unaggregated to preserve personalization while reducing energy consumption and communication overhead.Adapters and LoRA are used for local fine-tuning.Source: C. Key Innovations of PWFF · p. 4
  • Two reward models score helpfulness and harmlessness, and clients linearly weight them to personalize fine-tuning to their preferences.Source: C. Key Innovations of PWFF · p. 4
  • The personalized RL loss combines two immediate-reward models with two critics that predict future rewards, supporting client-specific value estimates and stable, effective actor updates [11].Source: C. Key Innovations of PWFF · p. 4

D. The Detailed Workflow of PWFF

  • Instruction tuning initializes a server LLM with adapters; clients configure local LoRA, tune on instruction data, transmit adapters for aggregation, and repeat until convergence.Source: D. The Detailed Workflow of PWFF · pp. 4–5
  • Clients train separate helpfulness and harmlessness reward models from preference data, send their parameters to the server for aggregation, and repeat until convergence.The workflow describes a small fully connected network mapping LLM outputs to numerical rewards.Source: D. The Detailed Workflow of PWFF · p. 5
  • During alignment, clients combine reward and critic models by preference, estimate local personalized loss, and use PPO to update local parameters; the server aggregates adapters for repeated rounds.Source: D. The Detailed Workflow of PWFF · p. 5

A. Simulation Settings

The simulations use a ten-client wireless PFL setting and evaluate PWFF with Alpaca-based instruction tuning, simulated preference data, and personalized alignment.

A. Simulation Settings

  • The simulated system has ten clients with non-IID data and different preferences, a resource-rich server, and Rayleigh channels at 5 dB SNR, 1 W transmission power, and 1 MHz bandwidth.Source: A. Simulation Settings · p. 5
  • Evaluation uses Alpaca, GPT-2, 36 adapters of bottleneck size 10, and 12–36 client-designed LoRA modules of rank 96.Source: A. Simulation Settings · p. 5
  • GPT-4 simulates human rankings for helpfulness and harmlessness; DistilBert reward models have 66M parameters, and client loss weights are randomly assigned to sum to 1.Source: A. Simulation Settings · p. 5

B. Evaluation Results

PWFF achieves the highest accuracy among evaluated instruction-tuning methods and relatively lower communication delay than vanilla FL. In alignment tuning, it obtains higher rewards than SFL and Shepherd, while the discussion identifies wireless instability and overfitting risks.

B. Evaluation Results

  • PWFF achieves the highest accuracy and relatively lower communication delay than vanilla FL in federated instruction tuning.The comparison includes vanilla FL, Fedada, and FedLora; PWFF transmits only adapter parameters.Source: B. Evaluation Results · p. 6
  • PFIT reduces energy consumption by 43% versus PFL, while Shepherd has the lowest energy consumption and the lowest reward.PWFF also obtains higher rewards than SFL and Shepherd.Source: B. Evaluation Results · p. 6

1) Wireless Aggregation and Divergence:

  • Wireless signal fluctuations can disrupt communication or lose data, potentially causing model divergence as participant contributions vary.The discussion calls for asynchronous aggregation and equitable client selection.Source: 1) Wireless Aggregation and Divergence · p. 6
  • Overly detailed personalization may cause overfitting to client data and compromise performance on other clients or tasks.The passage identifies regularization and control during fine-tuning as ways to balance personalization and generalization.Source: 3) Communication Efficiency and Model… · p. 6

2) Personalization and Overfitting:

Wireless PFL can incur substantial communication overhead, especially with numerous participants. PWFF combines partial aggregation and personalized fine-tuning, and the evaluation visuals cover communication, accuracy, reward, and energy measures.

3) Communication Efficiency and Model Accuracy:

  • Communication and collaboration can create substantial overhead as participant numbers grow; frequent exchanges increase latency and resource use.Communication instability can also cause model-update loss or delay.Source: 3) Communication Efficiency and Model… · pp. 6–7
  • The evaluation visuals cover accuracy and communication delay, and reward and energy consumption, for PWFF and its contenders.Source: Figure 4 · p. 7, Figure 5 · p. 7

VII. CONCLUSION

  • PWFF uses global adapters for information exchange and local LoRAs for customization, with partial aggregation intended to reduce communication delay and transmission energy.The framework also uses helpfulness and harmlessness reward models with local multi-objective alignment.Source: §VII · p. 7
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