5 papers · 1 filter
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…
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…
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…
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Roie Reshef, Kfir Y. Levy
This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze…
Verification of Neural Networks Local Differential Classification Privacy
Roie Reshef, Anan Kabaha, Olga Seleznova +1
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy pro…