354 citations · 693 across the 36 of their papers we have counts for
10 papers · 1 filter
Coded Distributed Computing for Hierarchical Multi-task Learning
Haoyang Hu, Songze Li, Minquan Cheng +1
In this paper, we consider a hierarchical distributed multi-task learning (MTL) system where distributed users wish to jointly learn different models orchestrated by a central serv…
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated learning (FL) has achieved great success as a privacy-preserving distributed training paradigm, where many edge devices collaboratively train a machine learning model by…
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Jiawei Shao, Yuchang Sun, Songze Li +1
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data. Different from centralized training,…
Secure Embedding Aggregation for Federated Representation Learning
Jiaxiang Tang, Jinbao Zhu, Songze Li +1
We consider a federated representation learning framework, where with the assistance of a central server, a group of distributed clients train collaboratively over their privat…
Secure Federated Clustering
Songze Li, Sizai Hou, Baturalp Buyukates +1
We consider a foundational unsupervised learning task of -means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients…
Information-Theoretically Private Matrix Multiplication From MDS-Coded Storage
Jinbao Zhu, Songze Li, Jie Li
We study two problems of private matrix multiplication, over a distributed computing system consisting of a master node, and multiple servers that collectively store a family of pu…
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li +1
We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of distributed users, ea…
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li +1
We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of distributed users, each of size ,…
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Yuchang Sun, Jiawei Shao, Songze Li +2
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning mod…
A Systematic Approach towards Efficient Private Matrix Multiplication
Jinbao Zhu, Songze Li
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix Multiplication (FPMM), for which matrices privately selected by a master node a…