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Found 7 papers, 1 papers with code
Efficient Online Unlearning via Hessian-Free Recollection of Individual Data Statistics
In this work, we propose a Hessian-free online unlearning method.
A Stochastic Quasi-Newton Method for Non-convex Optimization with Non-uniform Smoothness
Leveraging gradient clipping and variance reduction, our algorithm can achieve the best-known $\mathcal{O}(\epsilon^{-3})$ sample complexity and enjoys convergence speedup with simple hyperparameter tuning.
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
While the existing literature has studied extensively the generalization performances of centralized machine learning algorithms, similar analysis in the federated settings is either absent or with very restrictive assumptions on the loss functions.
Collaborative Multi-Agent Video Fast-Forwarding
We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning
Most existing federated minimax algorithms either require communication per iteration or lack performance guarantees with the exception of Local Stochastic Gradient Descent Ascent (SGDA), a multiple-local-update descent ascent algorithm which guarantees convergence under a diminishing stepsize.
Faithful Edge Federated Learning: Scalability and Privacy
To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy.
Distributed Multi-agent Video Fast-forwarding
In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness.
