5 papers
Balancing Privacy, Robustness, and Efficiency in Machine Learning
Youssef Allouah, Rachid Guerraoui, John Stephan
This position paper argues that achieving robustness, privacy, and efficiency simultaneously in machine learning systems is infeasible under prevailing threat models. The tension b…
Towards Trustworthy Federated Learning with Untrusted Participants
Youssef Allouah, Rachid Guerraoui, John Stephan
Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assu…
ByzFL: Research Framework for Robust Federated Learning
Marc González, Rachid Guerraoui, Rafael Pinot +3
We present ByzFL, an open-source Python library for developing and benchmarking robust federated learning (FL) algorithms. ByzFL provides a unified and extensible framework that in…
Adaptive Gradient Clipping for Robust Federated Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta +3
Robust federated learning aims to maintain reliable performance despite the presence of adversarial or misbehaving workers. While state-of-the-art (SOTA) robust distributed gradien…
CrowdProve: Community Proving for ZK Rollups
John Stephan, Matej Pavlovic, Antonio Locascio +1
Zero-Knowledge (ZK) rollups have become a popular solution for scaling blockchain systems, offering improved transaction throughput and reduced costs by aggregating Layer 2 transac…