Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

ArXi:2505.06335v2 Announce Type: replace-cross Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI such as LLMs to be trained across graphically diverse data. Central to this being efficient is the ability for FL to perform sparse gradient updates and remote direct memory access at the central server. Most of the research in FL security focuses on protecting data privacy at the edge client or in the communication channels between the client and server.