71 citations · 249 across the 13 of their papers we have counts for
13 papers
Edge-cloud Collaborative Learning with Federated and Centralized Features
Zexi Li, Qunwei Li, Yi Zhou +3
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud serve…
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zexi Li, Tao Lin, Xinyi Shang +1
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and p…
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?
Shuang Luo, Yinchuan Li, Jiahui Li +4
Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most ma…
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
Zexi Li, Jiaxun Lu, Shuang Luo +6
In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance. Thus, c…
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Jiangchao Yao, Shengyu Zhang, Yang Yao +15
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the co…
Unified Group Fairness on Federated Learning
Fengda Zhang, Kun Kuang, Yuxuan Liu +6
Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of e…