3 papers
cs.LG2026
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Tehila Dahan, Bassel Hamoud, Roie Reshef +2
Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as Local SGD, Minibatch SGD, an…
cs.LG2026
Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum
Tehila Dahan, Roie Reshef, Sharon Goldstein +1
Asynchronous stochastic gradient descent (SGD) enables scalable distributed training but suffers from gradient staleness. Existing mitigation strategies, such as delay-adaptive lea…
cs.LG2025
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation
Roie Reshef, Kfir Yehuda Levy
This paper tackles the challenge of achieving Differential Privacy (DP) in Federated Learning (FL) under partial-participation, where only a subset of the machines participate in e…