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Reinforcement Learning
arXiv (Cornell University)
STSA: Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
2025
Federated Class-Incremental Learning (FCIL) enables Class-Incremental Learning (CIL) from distributed data. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal c…
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Open Reinforcement Learning Open Article Page

Reinforcement Learning

Field of machine learning

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
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arXiv (Cornell University)
STSA: Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
2025
Federated Class-Incremental Learning (FCIL) enables Class-Incremental Learning (CIL) from distributed data. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal client drift caused by data heterogeneity and often incur significant computational and communication overhead, limiting practical deployment. To address these challenges simultaneously, we propose a novel approach, Spatial-Temporal Statistics Aggregation (STSA…
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WTopic
Reinforcement Learning
arXiv (Cornell University)
STSA: Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
2025
Federated Class-Incremental Learning (FCIL) enables Class-Incremental Learning (CIL) from distributed data. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal c…
Article
Critical
Symbolic
Virtual
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Open Reinforcement Learning Open Article Page