Aug 2, 2024 · Abstract: Federated Learning (FL) is to allow multiple clients to collaboratively train a model while keeping their data locally.
Sep 9, 2024 · We conducted extensive experiments on several datasets of which the results demonstrate that SR-FDIL outperforms state-of-the-art methods by up ...
Mar 9, 2024 · We propose to employ a simple, generic framework for FIL named Re-Fed, which can coordinate each client to cache important samples for replay.
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning pp. 1879-1890. Logical Synchrony and the Bittide Mechanism pp. 1936-1948. Beyond Belady ...
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning. Y Li, W Xu, H Wang, Y Qi, R Li, S Guo. IEEE Transactions on Parallel and Distributed ...
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning. Y Li, W Xu, Y Qi, H Wang, R Li, S Guo. IEEE Transactions on Parallel and Distributed ...
To tackle this challenge, we propose Federated Domain-Incremental Learning via Synergistic Replay (SR-FDIL), which alleviates catastrophic forgetting by ...
Jul 18, 2024 · This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client continues to learn incremental tasks where their domain ...
Missing: SR- | Show results with:SR-
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning ... Federated Learning (FL) is to allow multiple clients to collaboratively train a model ...
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning · Redundancy-Free and Load-Balanced TGNN Training With Hierarchical ...