139 citations · 323 across the 5 of their papers we have counts for
5 papers
FedDANE: A Federated Newton-Type Method
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated learning aims to jointly learn statistical models over massively distributed remote devices. In this work, we propose FedDANE, an optimization method that we adapt from D…
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Tian Li, Zaoxing Liu, Vyas Sekar +1
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection e…
Enhancing the Privacy of Federated Learning with Sketching
Zaoxing Liu, Tian Li, Virginia Smith +1
In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data local…
One-Shot Federated Learning
Neel Guha, Ameet Talwalkar, Virginia Smith
We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing…
Communication-Efficient Distributed Dual Coordinate Ascent
Martin Jaggi, Virginia Smith, Martin Takáč +4
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a commun…