Mixed Nash for Robust Federated Learning
We study robust federated learning (FL) within a game theoretic framework to alleviate the
server vulnerabilities to even an informed adversary who can tailor training-time attacks …
server vulnerabilities to even an informed adversary who can tailor training-time attacks …
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
In today's data-driven landscape, the delicate equilibrium between safeguarding user
privacy and unleashing data potential stands as a paramount concern. Federated learning …
privacy and unleashing data potential stands as a paramount concern. Federated learning …
Attacks on robust distributed learning schemes via sensitivity curve maximization
Distributed learning paradigms, such as federated or decentralized learning, allow a
collection of agents to solve global learning and optimization problems through limited local …
collection of agents to solve global learning and optimization problems through limited local …
[PDF][PDF] Cybersecurity Challenges in the Age of AI: New Attack and Defense Opportunities
J Li - 2024 - kilthub.cmu.edu
Recent decades have seen the unprecedented success of Artificial Intelligence (AI), with its
impact resonating beyond the confines of the technology sector to influence fields as diverse …
impact resonating beyond the confines of the technology sector to influence fields as diverse …
A Simulation-based Framework for Robust Federated Learning to Training-time Attacks
Well-known robust aggregation schemes in federated learning (FL) are shown to be
vulnerable to an informed adversary who can tailor training-time attacks [Fang et al., Xie et …
vulnerable to an informed adversary who can tailor training-time attacks [Fang et al., Xie et …