FLAP: Federated Learning with Attack and Privacy Awareness
Abstract
Federated learning provides data privacy protection by keeping data used for clients’ machine learning training private, and only sending model parameters updates to the centralised server/aggregator. However, the federated learning framework is still vulnerable to various attacks, such as data poisoning, launched by malicious/compromised clients. Cautious clients participating in federated learning, on the other hand, employ privacy protection techniques such as differential privacy to keep their model updates safe from inference attacks launched by the centralised aggregator. An aggregator thus needs to employ techniques to differentiate between model updates from benign, malicious and cautious clients, and to mitigate the effects of updates from clients other than benign clients. To reach this goal, we propose a novel federated learning system called FLAP which is robust against attacks launched by malicious clients and privacy protections employed by cautious clients.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.