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01/07/2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning is the network communicat...
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03/08/2021
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
We study a family of algorithms, which we refer to as local update metho...
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07/02/2020
On the Outsized Importance of Learning Rates in Local Update Methods
We study a family of algorithms, which we refer to as local update metho...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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11/30/2019
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Federated Learning enables mobile devices to collaboratively learn a sha...
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09/27/2019
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Federated Learning (FL) refers to learning a high quality global model b...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/04/2019
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enab...
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01/27/2019
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
In this work we present a randomized gossip algorithm for solving the av...
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12/18/2018
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Communication on heterogeneous edge networks is a fundamental bottleneck...
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12/03/2018
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, ...
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08/24/2016
AIDE: Fast and Communication Efficient Distributed Optimization
In this paper, we present two new communication-efficient methods for di...
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11/05/2015
Stop Wasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance ...
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04/16/2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
We propose mS2GD: a method incorporating a mini-batching scheme for impr...
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10/17/2014
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
We propose a mini-batching scheme for improving the theoretical complexi...
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12/15/2013
One-Shot-Learning Gesture Recognition using HOG-HOF Features
The purpose of this paper is to describe one-shot-learning gesture recog...
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12/05/2013