[Paper] Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception

Source: arXiv - 2606.10774v1

Overview

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underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information.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.

Key Contributions

This paper presents research in the following areas:

• cs.LG
• cs.DC

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.LG.

Authors

• Chanuka A. S. Hewa Kaluannakkage
• Rajkumar Buyya

Paper Information

• arXiv ID: 2606.10774v1
• Categories: cs.LG, cs.DC
• Published: June 9, 2026
• PDF: Download PDF