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FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
Runze You, Shi Pu
Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family…
In-in worldline formalism in pair creating fields
Patrick Copinger, Shi Pu
An in-in framework under Schwinger pair creating fields in strong-field quantum electrodynamics is formulated using in-out propagators in coordinate space, that have first-quantize…
Stochastic Push-Pull for Decentralized Nonconvex Optimization
Runze You, Shi Pu
To understand the convergence behavior of the Push-Pull method for decentralized optimization with stochastic gradients (Stochastic Push-Pull), this paper presents a comprehensive…
Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time
Runze You, Shi Pu
We study a distributed learning problem in which agents, each with potentially heterogeneous local data, collaboratively minimize the sum of their local cost functions via peer…