Quality Inference in Federated Learning with Secure Aggregation

2020/07/13 by Balázs Pejó, Pejó, Balázs, Biczók, Gergely · 2 citations
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2007.06236

openalex publication_date 2020/07/13 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to detect misbehaviours, to stabilize training performance, and to measure the individual contributions of participants.

Cited by

• On the Fragility of Contribution Score Computation in Federated Learning
• Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning

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