368 citations · 1k across the 46 of their papers we have counts for
5 papers · 2 filters
FedJAX: Federated learning simulation with JAX
Jae Hun Ro, Ananda Theertha Suresh, Ke Wu
Federated learning is a machine learning technique that enables training across decentralized data. Recently, federated learning has become an active area of research due to an inc…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Communication-Efficient Agnostic Federated Averaging
Jae Ro, Mingqing Chen, Rajiv Mathews +2
In distributed learning settings such as federated learning, the training algorithm can be potentially biased towards different clients. Mohri et al. (2019) proposed a domain-agnos…
Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath +1
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset drawn i.i.d. from an unknown distribution, and outputs a model $\widehat…
Learning with User-Level Privacy
Daniel Levy, Ziteng Sun, Kareem Amin +4
We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samp…