Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities

Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, communication-efficient training process with no risk of single-point failure. However, the role of structural and temporal inhomogeneities in such fully decentralised settings remains poorly understood. Here, we investigate their effects when model parameters are locally averaged during aggregation. We show that the de

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ethics.ai (3 July 2026), “Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities,” evidence record 282, https://ethics.ai/record/282 (originally published by arXiv).

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