FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning

Understand

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data.

• However, FL has been shown to be susceptible to backdoor and inference attacks.
• While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages clients' local models to deduce their private data.
• Contemporary solutions to address the security concerns of FL are either impractical for real-world deployment due to high-performance overheads or are tailored towards addressing specific threats, for instance, privacy-preserving aggregation or backdoor defenses.