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Found 11 papers, 1 papers with code
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning
The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity.
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift.
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 convergence.
Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines.
Federated Distributionally Robust Optimization for Phase Configuration of RISs
In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.
Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power.
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
In this paper, we propose an energy-efficient federated meta-learning framework.
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth.
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).
