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FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol
The NOSTR is a communication protocol for the social web, based on the w3c websockets standard.
Strong Duality Relations in Nonconvex Risk-Constrained Learning
Our results in particular imply zero duality gaps within the class of problems under study, both extending and improving on the state of the art in (risk-neutral) constrained learning.
Federated Learning Under Restricted User Availability
Federated Learning (FL) is a decentralized machine learning framework that enables collaborative model training while respecting data privacy.
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Methods for carefully selecting or generating a small set of training data to learn from, i. e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks.
Select without Fear: Almost All Mini-Batch Schedules Generalize Optimally
We establish matching upper and lower generalization error bounds for mini-batch Gradient Descent (GD) training with either deterministic or stochastic, data-independent, but otherwise arbitrary batch selection rules.
