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04/26/2023
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
In a vertical federated learning (VFL) system consisting of a central se...
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04/25/2023
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
In a federated learning (FL) system, distributed clients upload their lo...
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04/20/2023
Scene Style Text Editing
In this work, we propose a task called "Scene Style Text Editing (SSTE)"...
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04/15/2023
Multi-Server Secure Aggregation with Unreliable Communication Links
In many distributed learning setups such as federated learning (FL), cli...
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04/11/2023
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
The transferability of adversarial examples is a crucial aspect of evalu...
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12/16/2022
Coded Distributed Computing for Hierarchical Multi-task Learning
In this paper, we consider a hierarchical distributed multi-task learnin...
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11/08/2022
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
Federated learning (FL) has achieved great success as a privacy-preservi...
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10/06/2022
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Federated learning (FL) strives to enable collaborative training of mach...
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06/18/2022
Fully Privacy-Preserving Federated Representation Learning via Secure Embedding Aggregation
We consider a federated representation learning framework, where with th...
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05/31/2022
Secure Federated Clustering
We consider a foundational unsupervised learning task of k-means data cl...
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05/03/2022
Private Matrix Multiplication From MDS-Coded Storage With Colluding Servers
In this paper, we study the two problems of Private and Secure Matrix Mu...
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04/24/2022
Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff
We consider the problem of evaluating arbitrary multivariate polynomials...
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03/24/2022
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
We propose SwiftAgg+, a novel secure aggregation protocol for federated ...
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02/08/2022
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
We propose SwiftAgg, a novel secure aggregation protocol for federated l...
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01/25/2022
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Federated learning (FL) has attracted much attention as a privacy-preser...
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01/03/2022
A Systematic Approach towards Efficient Private Matrix Multiplication
We consider the problems of Private and Secure Matrix Multiplication (PS...
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09/29/2021
LightSecAgg: Rethinking Secure Aggregation in Federated Learning
Secure model aggregation is a key component of federated learning (FL) t...
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07/12/2021
OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
We propose OmniLytics, a blockchain-based secure data trading marketplac...
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11/22/2020
TaiJi: Longest Chain Availability with BFT Fast Confirmation
Most state machine replication protocols are either based on the 40-year...
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10/02/2019
Coded Merkle Tree: Solving Data Availability Attacks in Blockchains
In this paper, we propose coded Merkle tree (CMT), a novel hash accumula...
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06/26/2019
Coded State Machine -- Scaling State Machine Execution under Byzantine Faults
We introduce an information-theoretic framework, named Coded State Machi...
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11/08/2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address s...
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11/08/2018
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural ne...
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09/27/2018
PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously
Today's blockchains do not scale in a meaningful sense. As more nodes jo...
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05/24/2018
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
We consider the problem of training a least-squares regression model on ...
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05/05/2018
Compressed Coded Distributed Computing
Communication overhead is one of the major performance bottlenecks in la...
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10/27/2017
