cs.DCSep 13, 2026

Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data

Authors: Changheng Wang, Xianchao Zhang, Zhiqing Wei, Lingzhu Zhao, Zhongming Yang, Zhiyong Feng

Organizations: Key Labora-tory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China · Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314001, China · School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710000, China · School of Information Science and Engineering, Southeast University, Nanjing 210096, China

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Abstract

In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further affected by unreliable wireless links, as transmission errors may invalidate model updates. To address these challenges, we propose a federated random walk averaging (FedRW) framework, which is a variant of federated averaging (FedAvg) that mitigates data heterogeneity by updating models along random walk (RW) paths and aggregating them at the server. Model parameters are transmitted in packets with retransmission support to improve training quality by mitigating wireless errors along RW paths. Meanwhile, wireless transmission delays hinder the exploration of FedRW. To this end, we formulate a joint optimization problem that integrates learning, RW path selection, and transmission parameter tuning, aiming to minimize the training loss under delay constraints. By deriving an upper bound on the expected convergence of FedRW over unreliable wireless networks, we reduce the problem to a general form agnostic to task type and model architecture. A distributed solution is then proposed, in which the server or clients optimize packet size and maximum number of retransmissions locally, and efficiently select reliable and expandable next-hop nodes via a resilience-aware beam search with dynamic pruning. Simulation results show that FedRW achieves 2.26%-9% higher accuracy than state-of-the-art baselines under high data heterogeneity. Furthermore, the jointly optimized FedRW yields at least 2.78% higher accuracy and faster convergence compared to baselines.

Figures & tables

MethodologyCore MechanismLimitation / Gap
Data Heterogeneity Solutions
Regularization [ 3 , 7 , 8 , 9 ]Local objective constraintHyperparameter sensitivity
Clustered FL [ 10 , 11 , 12 , 13 ]Similarity-based groupingCold-start & high overhead
Meta-learning [ 14 , 15 , 16 ]Few-shot adaptabilityHessian-based complexity
SFL [ 17 ]Centralized-Fed hybridParadigm-specific tuning
Data-centric [ 18 , 19 , 20 ]Distribution alignmentPrivacy & bias concerns
TABLE I: Technical Comparison of Related Methodologies.
Fig. 1: Illustration of the FedRW structure over a reliable wireless network with M=3M=3 RW chains and Q=3Q=3 clients per chain.
Wireless ParametersAlgorithmic Parameters
ParameterValueParameterValue
BB10 MHzUU100
PP23 dBmMM5
N0N_{0}−- 174 dBm/Hz\mathrm{dBm/Hz}QQ3
ϵ\epsilon1e-5Batch size64
γτ\gamma_{\tau}20 msλ\lambda0.01
TABLE II: Simulation Parameters
Fig. 2: MNIST classification accuracy vs. total communication overhead under Non-IID data distribution (200 rounds).
Fig. 3: Classification accuracy and training loss on Fashion-MNIST, CIFAR-10, and CIFAR-100 in mixed Non-IID settings.
Fig. 4: MNIST classification accuracy of FedRW in Dirichlet Non-IID ( ζd\zeta_{d} =0.1) setting with varying random walk chain counts and lengths.
Fig. 5: Performance comparison between FedRW and state-of-the-art baselines in Dirichlet Non-IID setting.
Fig. 6: Performance comparison of FedRW under varying degrees of intra-chain data diversity.
Fig. 7: Comparison between unweighted and weighted aggregation schemes.
Fig. 8: Comparative of classification accuracy and loss between the proposed joint RW path selection and transmission parameter optimization algorithm and baselines on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100.
Fig. 9: Comparison of classification accuracy between the proposed algorithm and its variant with constraint ( c ) relaxed.
Fig. 10: Comparison of number of aggregated RW chains under different network conditions on MNIST in mixed Non-IID ( ζn\zeta_{n} =0) setting.
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