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cs.LG2026
Optimizing Stochastic Gradient Push under Broadcast Communications
Tuan Nguyen, Ting He
We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communications, with focus on mixing matr…
cs.LG2026
Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
Zhipeng Chang, Ting He, Wenrui Hao
Federated learning aggregates model updates from distributed clients, but standard first order methods such as FedAvg apply the same scalar weight to all parameters from each clien…
cs.LG2024
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks
Yudi Huang, Tingyang Sun, Ting He
The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly lear…