32 citations · 54 across the 6 of their papers we have counts for
6 papers
LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks
Handi Chen, Rui Zhou, Yun-Hin Chan +3
Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it bec…
Synergizing Foundation Models and Federated Learning: A Survey
Shenghui Li, Fanghua Ye, Meng Fang +4
Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models…
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Yun-Hin Chan, Rui Zhou, Running Zhao +2
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handl…
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
Yun-Hin Chan, Zhihan Jiang, Jing Deng +1
Federated learning (FL) facilitates edge devices to cooperatively train a global shared model while maintaining the training data locally and privately. However, a common assumptio…
Exploiting Features and Logits in Heterogeneous Federated Learning
Yun-Hin Chan, Edith C. -H. Ngai
Due to the rapid growth of IoT and artificial intelligence, deploying neural networks on IoT devices is becoming increasingly crucial for edge intelligence. Federated learning (FL)…
FedHe: Heterogeneous Models and Communication-Efficient Federated Learning
Chan Yun Hin, Ngai Edith
Federated learning (FL) is able to manage edge devices to cooperatively train a model while maintaining the training data local and private. One common assumption in FL is that all…