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Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

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arxiv 2307.04869 v2 pith:2VYS7RYQ submitted 2023-07-10 cs.LG cs.AIcs.CV

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

Gaurav Bagwe , Xiaoyong Yuan , Miao Pan , Lan Zhang This is my paper
classification cs.LG cs.AIcs.CV
keywords learningfed-cpromptcontinualpromptrehearsal-freeasynchronouscontrastivedata
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients. This paper focuses on rehearsal-free FCL, which has severe forgetting issues when learning new tasks due to the lack of access to historical task data. To address this issue, we propose Fed-CPrompt based on prompt learning techniques to obtain task-specific prompts in a communication-efficient way. Fed-CPrompt introduces two key components, asynchronous prompt learning, and contrastive continual loss, to handle asynchronous task arrival and heterogeneous data distributions in FCL, respectively. Extensive experiments demonstrate the effectiveness of Fed-CPrompt in achieving SOTA rehearsal-free FCL performance.

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Cited by 3 Pith papers

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  1. SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning

    cs.LG 2026-07 conditional novelty 6.0

    SUM projects client and task adaptation vectors to remove directional interference during server aggregation, improving federated class-incremental learning accuracy without client-side changes.

  2. Accurate and Resource-Efficient Federated Continual Learning

    cs.LG 2026-06 unverdicted novelty 6.0

    FedRAN achieves up to 4.8 pp higher accuracy in federated continual learning while using 30-122× less per-client communication by transmitting truncated-SVD summaries of random-feature Gram matrices and performing clo...

  3. Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

    cs.LG 2025-05 unverdicted novelty 6.0

    Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.