271 citations · 574 across the 44 of their papers we have counts for
5 papers · 1 filter
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
Neighborhood Matters: Influence Maximization in Social Networks with Limited Access
Chen Feng, Luoyi Fu, Bo Jiang +4
Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user's privacy conc…
Distributed Optimization over Block-Cyclic Data
Yucheng Ding, Chaoyue Niu, Yikai Yan +5
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Haolin Zhou, Chaoqi Yang, Xiaofeng Gao +3
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…