Hierarchical Split Federated Learning: Convergence Analysis and System Optimization

• URL: http://arxiv.org/abs/2412.07197v1
• Date: Tue, 10 Dec 2024 05:20:49 GMT
• Title: Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
• Authors: Zheng Lin, Wei Wei, Zhe Chen, Chan-Tong Lam, Xianhao Chen, Yue Gao, Jun Luo,
• Abstract summary: We analyze and optimize the learning performance of split federated learning (SFL) under multi-tier systems. We formulate a joint optimization problem for model splitting (MS) and model aggregation (MA) Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA for SFL within virtually any multi-tier system.
• Score: 21.617769534088477
• License:
• Abstract: As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloudedge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA subproblems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA for SFL within virtually any multi-tier system.

Related papers

• Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations [35.58487905412915]
  We propose a time-driven SFL (T-SFL) framework for heterogeneous systems. To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function. We develop a discriminative model selection algorithm that removes local models from clients whose number of iterations falls below a predetermined threshold.
  arXiv  Detail & Related papers  (2024-05-11T11:55:26Z)
• AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks [15.195798715517315]
  Split federated learning (SFL) is a promising solution by of floading the primary training workload to a server via model partitioning. We propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems.
  arXiv  Detail & Related papers  (2024-03-19T19:05:24Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients [44.89061671579694]
  Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and degrade performance. We propose nested federated learning (NeFL), a framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches.
  arXiv  Detail & Related papers  (2023-08-15T13:29:14Z)
• SplitFed resilience to packet loss: Where to split, that is the question [27.29876880765472]
  Split Federated Learning (SFL) aims to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. This paper investigates the robustness of SFL against packet loss on communication links. Experiments are carried out on a segmentation model for human embryo images and indicate the statistically significant advantage of a deeper split point.
  arXiv  Detail & Related papers  (2023-07-25T22:54:47Z)
• 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)
• Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence [83.58839320635956]
  Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recent FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. This paper addresses how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks.
  arXiv  Detail & Related papers  (2023-03-23T02:42:10Z)
• Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks [95.39148209543175]
  We propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation.
  arXiv  Detail & Related papers  (2023-03-19T06:00:05Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• 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)

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.