Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach

• URL: http://arxiv.org/abs/2502.17260v2
• Date: Wed, 26 Feb 2025 11:56:41 GMT
• Title: Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
• Authors: Yanmeng Wang, Wenkai Ji, Jian Zhou, Fu Xiao, Tsung-Hui Chang,
• Abstract summary: Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge.<n>Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies.<n>This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples.
• Score: 26.74684380975705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely hindered by unreliable wireless transmission and inherent data heterogeneity among clients. Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies, often focusing on uplink resource allocation across clients under the assumption of homogeneous client-server network standards. However, these approaches overlooked the fact that mobile clients may connect to the server via diverse network standards (e.g., 4G, 5G, Wi-Fi) with customized configurations, limiting the flexibility of server-side modifications and restricting applicability in real-world commercial networks. This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples, causing deviations from the global data distribution and introducing convergence bias in FL. Our analysis reveals that a carefully designed client selection strategy can mitigate biases induced by network unreliability and data heterogeneity. Motivated by this insight, we propose FedCote, a client selection approach that optimizes client selection probabilities without relying on wireless resource scheduling. Experimental results demonstrate the robustness of FedCote in DNN-based classification tasks under unreliable networks with frequent transmission failures.

Related papers

• Client Selection in Federated Learning with Data Heterogeneity and Network Latencies [19.161254709653914]
  Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a central server for global aggregation. In this paper, we propose two novel theoretically optimal client selection schemes that handle both these heterogeneities.
  arXiv  Detail & Related papers  (2025-04-02T17:31:15Z)
• FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling [12.067872131025231]
  Federated Learning (FL) enables collaborative model training across distributed clients without data sharing.<n>Current methods use dynamic pruning to improve efficiency by periodically adjusting sparse model topologies while maintaining sparsity.<n>We propose Federated Robust pruning via Thompson Sampling (FedRTS), a novel framework designed to develop robust sparse models.
  arXiv  Detail & Related papers  (2025-01-31T13:26:22Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks [0.5524804393257919]
  We propose the Bias-Aware Client Selection Algorithm (BACSA), which detects user bias and strategically selects clients based on their bias profiles. BACSA is suitable for sensitive healthcare applications where Quality of Service (QoS), privacy and security are paramount.
  arXiv  Detail & Related papers  (2024-11-01T21:34:43Z)
• Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients [21.104752782245257]
  Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm. This paper proposes a novel risk-aware accelerated FL framework that accounts for the clients heterogeneity in the amount of possessed data. The proposed scheme is benchmarked against a conservative scheme (i.e., only allowing trustworthy devices) and an aggressive scheme (i.e. oblivious to the trust metric)
  arXiv  Detail & Related papers  (2024-01-17T15:15:52Z)
• Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks [24.10349383347469]
  This study introduces a client selection strategy tailored to address non-IIDness in client data distributions. By strategically selecting clients whose data exhibit similar patterns for participation in FL training, our approach fosters a more uniform and representative data distribution. Our simulations demonstrate that this targeted client selection methodology significantly reduces the training loss of FL models in HAPS networks.
  arXiv  Detail & Related papers  (2024-01-10T18:22:00Z)
• 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)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• Quantized Federated Learning under Transmission Delay and Outage Constraints [30.892724364965005]
  Federated learning is a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge. In practical systems with limited radio resources, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO) We propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability.
  arXiv  Detail & Related papers  (2021-06-17T11:29:12Z)

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.