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math.OC2022
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent
Marco Bornstein, Jin-Peng Liu, Jingling Li +1
Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchro…
cs.DC2022★ 3 cited
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein, Tahseen Rabbani, Evan Wang +2
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train…