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Found 5 papers, 1 papers with code
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters.
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version.
Extended Radial Basis Function Controller for Reinforcement Learning
This paper proposes a hybrid reinforcement learning controller which dynamically interpolates a model-based linear controller and an arbitrary differentiable policy.
MQA: Answering the Question via Robotic Manipulation
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning
We obtain the optimal policy in closed form when gradient statistics are given.
