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Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation

arXiv 2608.13961 ↗First posted 14 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data; however, it faces significant communication bottlenecks and channel impairments in practice. Conventional network layer treatments either idealize the channel as error free or apply equal error protection (EEP) to transmitted model updates, failing to account for the inherently unequal importance of quantization bits within a single local model. To address this limitation, we propose a cross layer polar code based FL scheme that leverages the unequal error protection (UEP) property of polar codes under finite block lengths. Specifically, the proposed design selectively protects more significant quantization bits, thereby mitigating the detrimental effects of channel noise. We further provide a rigorous convergence analysis of the proposed scheme, deriving an upper bound on the convergence gap, which we then jointly optimize over the number of quantization bits and the polar code block length across all training iterations. Experimental results demonstrate that both constant and variable block length configurations of our polar code based scheme consistently achieve substantial performance gains over uncoded and LDPC-based EEP benchmarks, with the advantage becoming increasingly pronounced as the channel quality deteriorating. These findings confirm the efficacy of our cross-layer design in enhancing FL robustness and efficiency under realistic channel conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 14 Aug 2026.

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18 of 33 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  6. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  7. 2020Federated Learning With Quantized Global Model Updatescited by 55
  8. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  9. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  10. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  11. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 18
  12. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
6 more
  1. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  2. 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
  3. 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
  4. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  5. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  6. 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3

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  3. 2025FedBiF: Communication-Efficient Federated Learning via Bits Freezing
  4. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  5. 2025OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
  6. 2026A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
  7. 2024FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
  8. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks