ArticleScientific reports2025
Jointly exploring client drift and catastrophic forgetting in dynamic learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.Frontiers in robotics and AI · 2026Article
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Authors and funding
5 authors.
Funding
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Abstract
Federated and Continual Learning have emerged as promising paradigms for the privacy-aware use of Deep Learning in dynamic environments by addressing spatial and temporal constraints on data availability. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to ensuring robust performance. Existing work only addresses these problems separately, neglecting the fact that the root cause behind them, namely an unexpected shift in the data distribution, is connected. We propose a unified analysis framework for building a controlled test environment where we can jointly model spatial and temporal shifts, more closely emulating real dynamic settings. By generating a 3D landscape of the combined performance impact, we show that a moderate combination of both shifts can even improve the performance of the resulting model ("Generalization Bump"). We apply a simple and commonly used method from continual learning in the federated setting and observe this reoccurring phenomenon.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.