Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling

• URL: http://arxiv.org/abs/2505.23555v3
• Date: Sun, 13 Jul 2025 03:55:55 GMT
• Title: Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling
• Authors: Yanzhao Hou, Jiaxiang Geng, Boyu Li, Xiaofeng Tao, Juncheng Wang, Xiaodong Xu, Bing Luo,
• Abstract summary: Federated LoRA has emerged as a technique for efficiently fine-tuning large language models on distributed devices.<n>In this paper, we propose independent federated convergence wall-clock time of fine-tuning under both system and data heterogeneity.<n>Experiments demonstrate that our approach reduces wall-clock time compared to state-of-the-art methods across various models and datasets.
• Score: 15.218221234361922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated LoRA has emerged as a promising technique for efficiently fine-tuning large language models (LLMs) on distributed devices by reducing the number of trainable parameters. However, existing approaches often inadequately overlook the theoretical and practical implications of system and data heterogeneity, thereby failing to optimize the overall training efficiency, particularly in terms of wall-clock time. In this paper, we propose an adaptive federated LoRA strategy with independent client sampling to minimize the convergence wall-clock time of federated fine-tuning under both computation and communication heterogeneity. We first derive a new convergence bound for federated LoRA with arbitrary and independent client sampling, notably without requiring the stringent bounded gradient assumption. Then, we introduce an adaptive bandwidth allocation scheme that accounts for heterogeneous client resources and system bandwidth constraints. Based on the derived theory, we formulate and solve a non-convex optimization problem to jointly determine the LoRA sketching ratios and sampling probabilities, aiming to minimize wall-clock convergence time. An efficient and low-complexity algorithm is developed to approximate the solution. Finally, extensive experiments demonstrate that our approach significantly reduces wall-clock training time compared to state-of-the-art methods across various models and datasets.

Related papers

• Adaptive Deadline and Batch Layered Synchronized Federated Learning [66.93447103966439]
  Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous manner.<n>We propose ADEL-FL, a novel framework that jointly optimize per-round deadlines and user-specific batch sizes for layer-wise aggregation.
  arXiv  Detail & Related papers  (2025-05-29T19:59:18Z)
• Efficient Split Federated Learning for Large Language Models over Communication Networks [45.02252893286613]
  Fine-tuning pre-trained large language models (LLMs) in a distributed manner poses significant challenges on resource-constrained edge networks.<n>We propose SflLLM, a novel framework that integrates split federated learning with parameter-efficient fine-tuning techniques.<n>By leveraging model splitting and low-rank adaptation (LoRA), SflLLM reduces the computational burden on edge devices.
  arXiv  Detail & Related papers  (2025-04-20T16:16:54Z)
• Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks [27.545199007002577]
  Federated learning (FL) algorithms sample a fraction of clients in each round (partial participation) when the number of participants is large. Recent convergence analysis of FL have focused on slow-clock convergence due to client heterogeneity. We propose a new tractable convergence system for FL with arbitrary probability sampling.
  arXiv  Detail & Related papers  (2024-04-22T00:16:18Z)
• Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling [15.027267764009052]
  Federated Learning (FL) algorithms sample a random subset of clients to address the straggler issue and improve communication efficiency. Recent have proposed various client sampling methods, but they have limitations in joint system and data heterogeneity. We propose a new independent client sampling strategy to minimize the wall-clock time of FL.
  arXiv  Detail & Related papers  (2024-02-15T16:51:38Z)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy [25.763777765222358]
  Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. As the model size grows, the training latency due to limited transmission bandwidth and private information degrades while using differential privacy (DP) protection. We propose sparsification empowered FL framework wireless channels, in over to improve training efficiency without sacrificing convergence performance.
  arXiv  Detail & Related papers  (2023-04-09T05:21:15Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling [34.187387951367526]
  Federated learning (FL) algorithms usually sample a fraction in each (partial participation) when the number of participants is large. Recent works have focused on the convergence analysis of FL. We obtain new convergence bound for FL algorithms with arbitrary client sampling probabilities.
  arXiv  Detail & Related papers  (2021-12-21T14:28:40Z)
• Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization [85.84019017587477]
  Distributionally robust supervised learning is emerging as a key paradigm for building reliable machine learning systems for real-world applications. Existing algorithms for solving Wasserstein DRSL involve solving complex subproblems or fail to make use of gradients. We revisit Wasserstein DRSL through the lens of min-max optimization and derive scalable and efficiently implementable extra-gradient algorithms.
  arXiv  Detail & Related papers  (2021-04-27T16:56:09Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.