Federated Learning with Differential Privacy

2024/02/03 by Adrien Banse, Banse, Adrien, Jan Kreischer +3 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Data Security #Distributed #FOS: Computer and information sciences #I.2.11 #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2402.02230

openalex publication_date 2024/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in performance in a distributed and differentially private setting.

Cited by

• Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View
• PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy
• FedSelect-ME: A Secure Multi-Edge Federated Learning Framework with Adaptive Client Scoring

Related

• Federated Learning with Heterogeneous Differential Privacy
• Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
• Differentially Private Federated Learning: A Client Level Perspective
• Federated Learning With Differential Privacy: Algorithms and Performance Analysis
• Belt and Braces: When Federated Learning Meets Differential Privacy