Federated Continual Learning via Knowledge Fusion: A Survey

Author:

Yang Xin1ORCID,Yu Hao1ORCID,Gao Xin1ORCID,Wang Hao2ORCID,Zhang Junbo3ORCID,Li Tianrui4ORCID

Affiliation:

1. Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan, China

2. Nanyang Technological University, Singapore

3. JD iCity, JD Technology, Beijing, China

4. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China

Funder

Natural Science Foundation of Sichuan Province

Sichuan Science and Technology Program

Beijing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning;IEEE Transactions on Parallel and Distributed Systems;2024-11

2. Inference Load-Aware Orchestration for Hierarchical Federated Learning;2024 IEEE 49th Conference on Local Computer Networks (LCN);2024-10-08

3. Personalized Federated Continual Learning via Multi-Granularity Prompt;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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