首页|STSA: Federated Class-Incremental Learning via Spatial-Temporal
Statistics Aggregation
STSA: Federated Class-Incremental Learning via Spatial-Temporal
Statistics Aggregation
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原文链接
Arxiv
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), which provides
a unified framework to aggregate feature statistics both spatially (across
clients) and temporally (across stages). The aggregated feature statistics are
unaffected by data heterogeneity and can be used to update the classifier in
closed form at each stage. Additionally, we introduce STSA-E, a
communication-efficient variant with theoretical guarantees, achieving similar
performance to STSA-E with much lower communication overhead. Extensive
experiments on three widely used FCIL datasets, with varying degrees of data
heterogeneity, show that our method outperforms state-of-the-art FCIL methods
in terms of performance, flexibility, and both communication and computation
efficiency.
Arxiv
