39 citations · 59 across the 7 of their papers we have counts for
4 papers · 1 filter
From Narrow Unlearning to Emergent Misalignment: Causes, Consequences, and Containment in LLMs
Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna +5
Recent work has shown that fine-tuning on insecure code data can trigger an emergent misalignment (EMA) phenomenon, where models generate malicious responses even to prompts unrela…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Erum Mushtaq, Chaoyang He, Jie Ding +1
Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulat…
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Chaoyang He, Zhengyu Yang, Erum Mushtaq +3
Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data pr…