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underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information.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 Federate
The proliferation of decentralized AI applications and the increasing complexity of wireless networks necessitate robust solutions for federated learning challenges like data staleness and selection bias.
Improving decentralized federated learning makes AI more resilient, efficient, and accessible across varied infrastructure, impacting its deployment in critical and resource-constrained environments.
This research introduces concrete methods (DFL-AA) to mitigate fundamental architectural flaws in decentralized federated learning over unreliable networks, offering a path to more stable and unbiased model updates.
- · AI developers
- · Edge computing providers
- · Telecommunications companies
- · Decentralized application platforms
- · Centralized AI architectures that are less robust to network unreliability
More reliable and efficient decentralized AI systems become feasible and scalable.
This could accelerate the adoption of AI in areas with intermittent connectivity or strong privacy requirements.
Improved DFL resilience might reduce the need for centralized cloud infrastructure dependence for some AI tasks, potentially decentralizing compute further.
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Read at arXiv cs.LG