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Found 9 papers, 1 papers with code
Learning from straggler clients in federated learning
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay?
Multi-Message Shuffled Privacy in Federated Learning
This also resolves an open question on the optimal trade-off for private vector sum in the MMS model.
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In the shuffled model, we also achieve regret of $\tilde{O}(\sqrt{T}+\frac{1}{\epsilon})$ %for small $\epsilon$ as in the central case, while the best previously known algorithm suffers a regret of $\tilde{O}(\frac{1}{\epsilon}{T^{3/5}})$.
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms.
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
On the Renyi Differential Privacy of the Shuffle Model
The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model.
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework.
Successive Refinement of Privacy
This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)?
