Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks

• URL: http://arxiv.org/abs/2503.15559v1
• Date: Tue, 18 Mar 2025 22:11:54 GMT
• Title: Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks
• Authors: Haoran Gao, Samuel D. Okegbile, Jun Cai,
• Abstract summary: Split Federated Learning (SFL) offers a promising approach for distributed model training in edge computing.<n>We propose a collaborative SFL framework (CSFL) to optimize synchronization efficiency among users.<n>We show that our proposed CSFL framework reduces synchronization delays and improves overall system throughput.
• Score: 4.235050593084289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Split Federated Learning (SFL) offers a promising approach for distributed model training in edge computing, combining the strengths of split learning in reducing computational demands on edge devices and enhancing data privacy, with the role of federated aggregation to ensure model convergence and synchronization across users. However, synchronization issues caused by user heterogeneity have hindered the development of the framework. To optimize synchronization efficiency among users and improve overall system performance, we propose a collaborative SFL framework (CSFL). Based on the model's partitioning capabilities, we design a mechanism called the collaborative relay optimization mechanism (CROM), where the assistance provided by high-efficiency users is seen as a relay process, with the portion of the model they compute acting as the relay point. Wireless communication between users facilitates real-time collaboration, allowing high-efficiency users to assist bottleneck users in handling part of the model's computation, thereby alleviating the computational load on bottleneck users. Simulation results show that our proposed CSFL framework reduces synchronization delays and improves overall system throughput while maintaining similar performance and convergence rate to the SFL framework. This demonstrates that the collaboration not only reduces synchronization waiting time but also accelerates model convergence.

Related papers

• Efficient Federated Split Learning for Large Language Models over Communication Networks [14.461758448289908]
  Fine-tuning pre-trained large language models (LLM) in a distributed manner poses significant challenges on resource-constrained edge devices. We propose FedsLLM, a novel framework that integrates split federated learning with parameter-efficient fine-tuning techniques.
  arXiv  Detail & Related papers  (2025-04-20T16:16:54Z)
• FADAS: Towards Federated Adaptive Asynchronous Optimization [56.09666452175333]
  Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
  arXiv  Detail & Related papers  (2024-07-25T20:02:57Z)
• Federated Learning based on Pruning and Recovery [0.0]
  This framework integrates asynchronous learning algorithms and pruning techniques. It addresses the inefficiencies of traditional federated learning algorithms in scenarios involving heterogeneous devices. It also tackles the staleness issue and inadequate training of certain clients in asynchronous algorithms.
  arXiv  Detail & Related papers  (2024-03-16T14:35:03Z)
• Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach [16.093068118849246]
  This paper presents a novel split federated learning (SFL) framework that pairs clients with different computational resources. A greedy algorithm is proposed by reconstructing the optimization of training latency as a graph edge selection problem. Simulation results show the proposed method can significantly improve the FL training speed and achieve high performance.
  arXiv  Detail & Related papers  (2023-08-26T11:10:54Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Time-sensitive Learning for Heterogeneous Federated Edge Intelligence [52.83633954857744]
  We investigate real-time machine learning in a federated edge intelligence (FEI) system. FEI systems exhibit heterogenous communication and computational resource distribution. We propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model.
  arXiv  Detail & Related papers  (2023-01-26T08:13:22Z)
• Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks [56.91063444859008]
  Federated Learning (FL) is a collaborative machine learning framework that combines on-device training and server-based aggregation. We propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. We show that an age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.
  arXiv  Detail & Related papers  (2022-12-14T17:33:01Z)
• 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)
• Blockchain-enabled Server-less Federated Learning [5.065631761462706]
  We focus on an asynchronous server-less Federated Learning solution empowered by (BC) technology. In contrast to mostly adopted FL approaches, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates.
  arXiv  Detail & Related papers  (2021-12-15T07:41:23Z)
• Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning [72.78668894576515]
  Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework. We propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems.
  arXiv  Detail & Related papers  (2021-07-23T18:57:08Z)
• Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients [21.63719641718363]
  Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. This paper presents a novel FL algorithm, namely Hybrid Federated Learning (HFL), to achieve a learning balance in efficiency and effectiveness.
  arXiv  Detail & Related papers  (2021-02-12T02:27:44Z)

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.