Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets

• URL: http://arxiv.org/abs/2408.09762v1
• Date: Mon, 19 Aug 2024 07:43:35 GMT
• Title: Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
• Authors: Xingrun Yan, Shiyuan Zuo, Rongfei Fan, Han Hu, Li Shen, Puning Zhao, Yong Luo,
• Abstract summary: In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter (PS) is often a bottleneck. We propose sequential FL (SFL) HFL for the first time, which removes the central PS and enables the model to be completed only through passing data between two adjacent ESs for each server.
• Score: 25.010661914466354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical federated learning (HFL) that poses multiple edge servers (ESs) between clients and the PS can partially alleviate communication pressure but still needs the aggregation of model parameters from multiple ESs at the PS. To further reduce communication overhead, we bring sequential FL (SFL) into HFL for the first time, which removes the central PS and enables the model training to be completed only through passing the global model between two adjacent ESs for each iteration, and propose a novel algorithm adaptive to such a combinational framework, referred to as Fed-CHS. Convergence results are derived for strongly convex and non-convex loss functions under various data heterogeneity setups, which show comparable convergence performance with the algorithms for HFL or SFL solely. Experimental results provide evidence of the superiority of our proposed Fed-CHS on both communication overhead saving and test accuracy over baseline methods.

Related papers

• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. Experiments show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• 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)
• 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)
• Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees [18.24213566328972]
  Decentralized decentralized learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are carried out by the clients without a central server. DSpodFL consistently achieves speeds compared with baselines under various system settings.
  arXiv  Detail & Related papers  (2024-02-05T19:02:19Z)
• 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)
• Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis [15.311309249848739]
  Hierarchical learning (FL) has demonstrated promising scalability advantages over the traditional "star-topology" architecture-based federated learning (FL) In this paper, we propose independent sub training overconstrained Internet of Things (IoT) Key idea behind HIST is a global version of model computation, where we partition the global model into disjoint submodels in each round, and distribute them across different cells.
  arXiv  Detail & Related papers  (2023-10-27T04:42:59Z)
• 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)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• 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)

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.