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Found 4 papers, 1 papers with code
Attesting Distributional Properties of Training Data for Machine Learning
The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness.
HyFL: A Hybrid Framework For Private Federated Learning
Federated learning (FL) has emerged as an efficient approach for large-scale distributed machine learning, ensuring data privacy by keeping training data on client devices.
ScionFL: Efficient and Robust Secure Quantized Aggregation
In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS
Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.
