9 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.DC2026
Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation
Chenwang Liu, Yijun Liu, Chang Liu +2
Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited communication resources at the…
cs.DC2025★ 9 cited
Rethinking Knowledge Distillation in Collaborative Machine Learning: Memory, Knowledge, and Their Interactions
Pengchao Han, Xi Huang, Yi Fang +1
Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their priv…