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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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11/08/2022
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning
We consider private federated learning (FL), where a server aggregates d...
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05/30/2022
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Federated learning (FL) is an effective mechanism for data privacy in re...
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03/15/2022
Privacy-Aware Compression for Federated Data Analysis
Federated data analytics is a framework for distributed data analysis wh...
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11/08/2021
Papaya: Practical, Private, and Scalable Federated Learning
Cross-device Federated Learning (FL) is a distributed learning paradigm ...
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10/30/2021
Sustainable AI: Environmental Implications, Challenges and Opportunities
This paper explores the environmental impact of the super-linear growth ...
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10/15/2021
Trade-offs of Local SGD at Scale: An Empirical Study
As datasets and models become increasingly large, distributed training h...
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01/13/2021
Learning with Gradient Descent and Weakly Convex Losses
We study the learning performance of gradient descent when the empirical...
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11/05/2020
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
The paper proposes and optimizes a partial recovery training system, CPR...
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10/09/2019
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Effective network congestion control strategies are key to keeping the I...
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06/09/2019