Sixing Tan | Searcharxiv

arXiv2026

Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap

Federated Continual Learning (FCL) leverages inter-client collaboration to better balance new knowledge acquisition and old knowledge retention on non-stationary data. However, existing FCL methods struggle to adapt to streaming scenarios where sequential and ephemerally accessible data chunks lack task identifiers and exhibit arbitrary class overlap, leading to confusion between old and new knowledge and an inability to sustain local inference on all encountered classes. To address this, we propose FedKACE with three components: 1) an adaptive mechanism that determines when to switch the inference model from the local to the global one to improve client-side inference performance; 2) a responsive gradient-balanced replay scheme that utilizes the ratio of the squared L2 gradient norms to balance client-specific knowledge between new acquisition and old retention; 3) a holistic buffer maintenance strategy that preserves highly informative and boundary-significant samples to enhance knowledge retention under class overlap.Experiments across multiple scenarios and theoretical analysis demonstrate the effectiveness of FedKACE.

cs.LG↗

arXiv2024

Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage

Recently, a large number of data sources opened up by informatization intensify the data heterogeneity, the faster speed of data generation and the gradual implementation of data regulations limit the storage time of data. In personalized Federated Learning (pFL), clients train customized models to meet their personal objectives. However, due to the time-varying local data heterogeneity and the inaccessibility of previous data, existing pFL methods not only fail to solve the catastrophic forgetting of local models, but also difficult to estimate the degree of collaboration between clients. To address this issue, our core idea is a low consumption and high-quality generative replay architecture. Specifically, we decouple the generator by category to reduce the generation error of each category while mitigating catastrophic forgetting, use local model to improving the quality of generated data and reducing the update frequency of generator, and propose a local data reconstruction scheme to reduce data generation while adjusting the proportion of data categories. Based on above, we propose our pFL framework, pFedGRP, to achieve personalized aggregation and local knowledge transfer. Comprehensive experiments on five datasets with multiple settings show the superiority of pFedGRP over eight baseline methods.

cs.DC↗