1 citations · 1 across the 4 of their papers we have counts for
4 papers
MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
Heng Zhang, Xiaohong Deng, Sijing Duan +4
Low-Earth-orbit (LEO) satellite constellations are increasingly performing on-board computing. However, the continuous emergence of new classes under strict memory and communicatio…
Federated Unlearning in Edge Networks: A Survey of Fundamentals, Challenges, Practical Applications and Future Directions
Jer Shyuan Ng, Wathsara Daluwatta, Shehan Edirimannage +5
The proliferation of connected devices and privacy-sensitive applications has accelerated the adoption of Federated Learning (FL), a decentralized paradigm that enables collaborati…
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Liping Yi, Han Yu, Chao Ren +4
Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (n…
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
Liping Yi, Han Yu, Chao Ren +4
Federated learning (FL) has been widely adopted for collaborative training on decentralized data. However, it faces the challenges of data, system, and model heterogeneity. This ha…