activity
20172021
most citedA Field Guide to Federated Optimization

167 citations · 174 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2021167 cited

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…

cs.GT2020

Forming better stable solutions in Group Formation Games inspired by Internet Exchange Points (IXPs)

Elliot Anshelevich, Wennan Zhu

We study a coordination game motivated by the formation of Internet Exchange Points (IXPs), in which agents choose which facilities to join. Joining the same facility as other agen…

cs.AI20197 cited

Awareness of Voter Passion Greatly Improves the Distortion of Metric Social Choice

Ben Abramowitz, Elliot Anshelevich, Wennan Zhu

We develop new voting mechanisms for the case when voters and candidates are located in an arbitrary unknown metric space, and the goal is to choose a candidate minimizing social c…

cs.CR2019

Federated Heavy Hitters Discovery with Differential Privacy

Wennan Zhu, Peter Kairouz, Brendan McMahan +2

The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if no…

cs.MA2018

Ordinal Approximation for Social Choice, Matching, and Facility Location Problems given Candidate Positions

Elliot Anshelevich, Wennan Zhu

In this work we consider general facility location and social choice problems, in which sets of agents and facilities are located in a metric space, and…

cs.GT2017

Tradeoffs Between Information and Ordinal Approximation for Bipartite Matching

Elliot Anshelevich, Wennan Zhu

We study ordinal approximation algorithms for maximum-weight bipartite matchings. Such algorithms only know the ordinal preferences of the agents/nodes in the graph for their prefe…