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Evidence record 6593 · automatically gathered

FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation

In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers

Record details

Published: 30 March 2026
Source: arXiv
Category: Research
Topics: Privacy · Healthcare · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (30 March 2026), “FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation,” evidence record 6593, https://ethics.ai/record/6593 (originally published by arXiv).

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