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This research addresses critical bottlenecks in decentralized federated learning over lossy wireless networks by combining inverse probability weighting with age-of-information metrics to correct selection bias and update staleness. The proposed DFL-AA framework theoretically eliminates link-quality distortion in expectation while avoiding the need for global synchronization, offering consistent performance gains across heterogeneous network conditions. For practitioners building resilient distributed systems, this work provides a robust methodological foundation for handling partial model reception and asynchronous node contributions without relying on uniform gossip aggregation.
Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception
arXiv:2606.10774v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information. We show that uniform gossip aggregation with local-fill reconstruction introduces persistent link-quality-induced bias, while completeness-based weighting further amplifies this effect. To address these challenges, we propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which combines Inverse Probability Weighting with online EWMA-based channel estimation to correct selection bias and Age-of-Information-based weighting to mitigate staleness without requiring global synchronization. We theoretically show that DFL-AA removes link-quality distortion in expectation and experimentally demonstrate consistent improvements over state-of-the-art baselines across varying loss rates, network sizes, and heterogeneous wireless conditions.
Abstract:Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information. We show that uniform gossip aggregation with local-fill reconstruction introduces persistent link-quality-induced bias, while completeness-based weighting further amplifies this effect. To address these challenges, we propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which combines Inverse Probability Weighting with online EWMA-based channel estimation to correct selection bias and Age-of-Information-based weighting to mitigate staleness without requiring global synchronization. We theoretically show that DFL-AA removes link-quality distortion in expectation and experimentally demonstrate consistent improvements over state-of-the-art baselines across varying loss rates, network sizes, and heterogeneous wireless conditions.
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
arXiv:2606.10774 [cs.LG]
(or arXiv:2606.10774v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.10774 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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