main webpage W Topic 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 Open Learning Open Article Page

Learning

Process of acquiring new knowledge Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved. Human learning starts at birth (it might even start before in terms of an embryo's need for both interaction with, and freedom within its environment within the womb. Critical Symbolic Virtual Narrative Open Learning Open Article Page

Exploring foci of: 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… Open Article Page

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Explore Learning W Topic 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 Narrative Open Learning Open Article Page