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08/29/2022
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
In this paper, we propose to solve a regularized distributionally robust...
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06/17/2022
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Newton-type methods are popular in federated learning due to their fast ...
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10/04/2021
Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous dev...
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08/20/2021
Federated Distributionally Robust Optimization for Phase Configuration of RISs
In this article, we study the problem of robust reconfigurable intellige...
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06/02/2021
Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Split-learning (SL) has recently gained popularity due to its inherent p...
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06/02/2021
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
Today's intelligent applications can achieve high performance accuracy u...
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05/31/2021
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
In this paper, we propose an energy-efficient federated meta-learning fr...
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09/14/2020
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized mach...
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09/06/2020
Efficient Importance Sampling for the Left Tail of Positive Gaussian Quadratic Forms
Estimating the left tail of quadratic forms in Gaussian random vectors i...
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07/03/2020
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Wireless connectivity is instrumental in enabling scalable federated lea...
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09/24/2019
Efficient Estimation of the Left Tail of Bimodal Distributions with Applications to Underwater Optical Communication Systems
In this paper, we propose efficient importance sampling estimators to ev...
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01/26/2019