activity
20122022
most citedThroughput-Optimal Topology Design for Cross-Silo Federated Learning

50 citations · 84 across the 8 of their papers we have counts for

collaborators

14 papers

cs.LG202115 cited

Efficient passive membership inference attack in federated learning

Oualid Zari, Chuan Xu, Giovanni Neglia

In cross-device federated learning (FL) setting, clients such as mobiles cooperate with the server to train a global machine learning model, while maintaining their data locally. H…

cs.PF2021

A New Upper Bound on Cache Hit Probability for Non-anticipative Caching Policies

Nitish K. Panigrahy, Philippe Nain, Giovanni Neglia +1

Caching systems have long been crucial for improving the performance of a wide variety of network and web based online applications. In such systems, end-to-end application perform…

cs.NI2021

Content Placement in Networks of Similarity Caches

Michele Garetto, Emilio Leonardi, Giovanni Neglia

Similarity caching systems have recently attracted the attention of the scientific community, as they can be profitably used in many application contexts, like multimedia retrieval…

cs.LG202050 cited

Throughput-Optimal Topology Design for Cross-Silo Federated Learning

Othmane Marfoq, Chuan Xu, Giovanni Neglia +1

Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.…

cs.DC2020

Dynamic backup workers for parallel machine learning

Chuan Xu, Giovanni Neglia, Nicola Sebastianelli

The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of workers, which iteratively…

cs.LG20209 cited

Decentralized gradient methods: does topology matter?

Giovanni Neglia, Chuan Xu, Don Towsley +1

Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine…