5.1k citations · 7.5k across the 42 of their papers we have counts for
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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…
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Michael Kuchnik, George Amvrosiadis, Virginia Smith
Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conser…
Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar +1
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in he…
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami +1
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately a…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…