FLEDGE: Ledger-based Federated Learning Resilient to Inference and
  Backdoor Attacks
Jorge Castillo Phillip Rieger Hossein Fereidooni Qian Chen Ahmad Sadeghi
Federated learning (FL) is a distributed learning process that uses a trusted
aggregation server to allow multiple parties (or clients) to collaboratively
train a machine learning model without having them share their private data.
Recent research, however, has demonstrated the effectiveness of inference and
poisoning attacks on FL. Mitigating both attacks simultaneously is very
challenging. State-of-the-art solutions have proposed the use of poisoning
defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy
(DP). However, these techniques are not efficient and fail to address the
malicious intent behind the attacks, i.e., adversaries (curious servers and/or
compromised clients) seek to exploit a system for monetization purposes. To
overcome these limitations, we present a ledger-based FL framework known as
FLEDGE that allows making parties accountable for their behavior and achieve
reasonable efficiency for mitigating inference and poisoning attacks. Our
solution leverages crypto-currency to increase party accountability by
penalizing malicious behavior and rewarding benign conduct. We conduct an
extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and
CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong
privacy guarantees for model updates without sacrificing model utility; (2)
FLEDGE can successfully mitigate different poisoning attacks without degrading
the performance of the global model; and (3) FLEDGE offers unique reward
mechanisms to promote benign behavior during model training and/or model
aggregation.