ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices

• URL: http://arxiv.org/abs/2402.15903v2
• Date: Wed, 17 Apr 2024 02:59:30 GMT
• Title: ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
• Authors: Guangyu Zhu, Yiqin Deng, Xianhao Chen, Haixia Zhang, Yuguang Fang, Tan F. Wong,
• Abstract summary: Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. We propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server.
• Score: 22.664980594996155
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users' heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.

Related papers

• Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning [9.900317349372383]
  Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. We propose a two-tier HFEL system, where edge devices are connected to edge servers and edge servers are interconnected through peer-to-peer (P2P) edge backhauls. Our goal is to enhance the training efficiency of the HFEL system through strategic resource allocation and topology design.
  arXiv  Detail & Related papers  (2024-09-29T01:48:04Z)
• Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach [67.27031215756121]
  Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as emphConfederated Learning (CFL) in order to accommodate a larger number of users.
  arXiv  Detail & Related papers  (2024-02-28T03:27:10Z)
• Accelerating Split Federated Learning over Wireless Communication Networks [17.97006656280742]
  We consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL) We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.
  arXiv  Detail & Related papers  (2023-10-24T07:49:56Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining [119.89373423433804]
  Area Under Precision-Recall (AUPRC) was introduced as an effective metric. Serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck. We propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC.
  arXiv  Detail & Related papers  (2023-08-06T06:51:32Z)
• When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework [6.871107511111629]
  CPN-FedSL is a Federated Split Learning (FedSL) framework over Computing Power Network (CPN) We build a dedicated model to capture the basic settings and learning characteristics (e.g., latency, flow, convergence)
  arXiv  Detail & Related papers  (2023-05-22T12:36:52Z)
• 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)
• 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)
• Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point [51.47520726446029]
  cooperative edge learning (CE-FL) is a distributed machine learning architecture. We model the processes taken during CE-FL, and conduct analytical training. We show the effectiveness of our framework with the data collected from a real-world testbed.
  arXiv  Detail & Related papers  (2022-03-26T00:41:57Z)
• Towards Heterogeneous Clients with Elastic Federated Learning [45.2715985913761]
  Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. We propose Elastic Federated Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the system. It is an efficient and effective algorithm that compresses both upstream and downstream communications.
  arXiv  Detail & Related papers  (2021-06-17T12:30:40Z)

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.