Secure Aggregation

Secure aggregation (SA) aims to enable collaborative model training in federated learning (FL) without revealing individual client data to the central server. Current research focuses on developing efficient and robust SA protocols, often incorporating cryptographic techniques like homomorphic encryption or secret sharing, and addressing vulnerabilities to various attacks, including gradient inversion and membership inference. The effectiveness and practicality of SA are crucial for deploying privacy-preserving FL in sensitive applications like healthcare and finance, driving ongoing efforts to improve its efficiency, security, and compatibility with different model architectures and data distributions.

Papers

May 9, 2023

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh, Alexandre Graell i Amat
Person Identification Secure Aggregation Malicious Client Private Aggregation

April 25, 2023

Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things
Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman, Xun Yi, Mahathir Almashor
Federated Learning Secure Aggregation Model Aggregation Blockchain Network Trusted Execution Environment

March 27, 2023

March 21, 2023

LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
Joshua C. Zhao, Atul Sharma, Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Salman Avestimehr, Saurabh Bagchi
Federated Learning Secure Aggregation Data Reconstruction Attack Client Gradient Edited Model

March 7, 2023

Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche, Gergely Ács, Mario Fritz
Differential Privacy Secure Aggregation Privacy Attack Property Inference

February 22, 2023

Multi-Message Shuffled Privacy in Federated Learning
Antonious M. Girgis, Suhas Diggavi
Differential Privacy Secure Aggregation Private Gradient Privacy Accuracy Privacy Mechanism Private Mean Estimation

February 20, 2023

ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Giuseppe Caire
Secure Aggregation Secret Sharing Secure Computation Byzantine Robust Aggregation Commitment Mechanism Coded Computing

January 30, 2023

Efficient Node Selection in Private Personalized Decentralized Learning
Edvin Listo Zec, Johan Östman, Olof Mogren, Daniel Gillblad
Decentralized Learning Privacy Risk Secure Aggregation Adversarial Multi Armed Bandit Node Level Privacy Node Selection

January 9, 2023

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Differential Privacy Federated Learning Private Data Secure Aggregation Privacy Enhancing Technology Data Reconstruction

November 10, 2022

Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning
John Reuben Gilbert
Federated Learning Sensitive Data Secure Aggregation Data Privacy Noise Tolerance

October 22, 2022

Federated Calibration and Evaluation of Binary Classifiers
Graham Cormode, Igor Markov
Differential Privacy Global Evaluation Private Data Local Differential Privacy Secure Aggregation Binary Classifier Privacy Model Federated Hyperparameter

October 13, 2022

ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai
Secure Aggregation Modern News Aggregator

October 3, 2022

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation
Mohammad Taha Toghani, César A. Uribe
Potential Scalability Convergence Rate Secure Aggregation Unbounded Gradient Asynchronous Aggregation

September 16, 2022

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression
Xinlin Leng, Chenxu Li, Weifeng Xu, Yuyan Sun, Hongtao Wang
Gradient Descent Secure Aggregation Multi Party Private Estimation Federated Stochastic Greedy Coordinate

August 16, 2022

FedPerm: Private and Robust Federated Learning by Parameter Permutation
Hamid Mozaffari, Virendra J. Marathe, Dave Dice
Differential Privacy Secure Aggregation Adversarial Behavior Model Poisoning Attack

August 13, 2022

Trustworthy Federated Learning via Blockchain
Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, Kai Yang
Federated Learning Blockchain Based Platform Trustworthy Artificial Intelligence Secure Aggregation Byzantine Fault Tolerance

August 3, 2022

How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, Salman Avestimehr
Secure Aggregation Model Inversion Attack Privacy Protection Privacy Preserving Machine Learning FedAvg Algorithm

July 26, 2022

Reconciling Security and Communication Efficiency in Federated Learning
Karthik Prasad, Sayan Ghosh, Graham Cormode, Ilya Mironov, Ashkan Yousefpour, Pierre Stock
Federated Learning Secure Aggregation Communication Efficiency Cross Device Federated Learning

July 13, 2022

Enhanced Security and Privacy via Fragmented Federated Learning
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, Alberto Blanco-Justicia, David Sanchez
Privacy Policy Secure Aggregation Privacy Attack Split Federated Learning Security Measure Privacy Accuracy Certified Defense