445 citations · 830 across the 60 of their papers we have counts for
71 papers
The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys
Kai Wan, Hua Sun, Mingyue Ji +2
This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to a…
Fundamental Limits of Multi-Message Private Computation
Ali Gholami, Kai Wan, Tayyebeh Jahani-Nezhad +3
In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of files from servers without revealing the demanded…
Fundamental Limits of Distributed Linearly Separable Computation under Cyclic Assignment
Wenbo Huang, Kai Wan, Hua Sun +3
This paper studies the master-worker distributed linearly separable computation problem, where the considered computation task, referred to as linearly separable function, is a typ…
Federated Learning with Flexible Control
Shiqiang Wang, Jake Perazzone, Mingyue Ji +1
Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g.…
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Shiqiang Wang, Mingyue Ji
Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate i…
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Won Joon Yun, Yunseok Kwak, Hankyul Baek +5
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is chal…