D.S. Kuznetsov

FedTSV dynamically re-weights client updates in federated learning based on their impact on the global model's optimization trajectory, leading to faster convergence, increased robustness, and fairer contribution assessments.

LLM safety is more fragile than we thought: cleverly framed in-context examples can override learned refusal behaviors with alarming ease.