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Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
Yujie Gu, Richeng Jin, Zhaoyang Zhang +1
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In…
Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
Reza Jahani, Md Farhamdur Reza, Richeng Jin +1
Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central se…
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal…
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
Ferdous Pervej, Richeng Jin, Md Moin Uddin Chowdhury +3
Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors o…
Distribution-Aware Mobility-Assisted Decentralized Federated Learning
Md Farhamdur Reza, Reza Jahani, Richeng Jin +1
Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients ca…
Federated Learning Nodes Can Reconstruct Peers' Image Data
Ethan Wilson, Kai Yue, Chau-Wai Wong +1
Federated learning (FL) is a privacy-preserving machine learning framework that enables multiple nodes to train models on their local data and periodically average weight updates t…
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
Gabriel Thompson, Kai Yue, Chau-Wai Wong +1
Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL fac…