Federated Learning Algorithms to Optimize the Client and Cost ...

This paper discusses the current development status of federated learning. From the perspective of federated learning algorithms, the federated learning- ...

Federated Learning Algorithms to Optimize the - ProQuest

Federated Learning Algorithms to Optimize the Client and Cost Selections. Alferaidi, Ali; Yadav, Kusum; Alharbi, Yasser; Viriyasitavat, Wattana; Kautish ...

Ali Alferaidi & Kusum Yadav & Yasser Alharbi & Wattana Viriyasitavat & Sandeep Kautish & Gaurav Dhiman & Araz Darba, 2022. "Federated Learning Algorithms to ...

Grey Wolf Optimizer for Reducing Communication Cost of Federated ...

Abstract: Federated Learning (FL) is a type of Machine Learning (ML) technique in which only learned models are stored on a server to sustain data security.

Client selection and model pricing for federated learning in data ...

Improving the model training performance by optimizing the client selection. ... Proposing a model pricing framework for federated learning in ...

Federated Learning Algorithms to Optimize the Client and Cost ...

By Ali Alferaidi, Kusum Yadav, Yasser Alharbi, Wattana Viriyasitavat, Sandeep Kautish, Gaurav Dhiman and Araz Darba; Abstract: In recent years, federated ...

Dynamic Client Clustering, Bandwidth Allocation, and Workload ...

Abstract:Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to ...

How Valuable is Your Data? Optimizing Client Recruitment in ...

Abstract: Federated learning allows distributed clients to train a shared machine learning model while preserving user privacy.

Reducing communication in federated learning via efficient client ...

Federated learning (FL) ameliorates privacy concerns in settings where a central server coordinates learning from data distributed across many clients; rather ...

Research review of federated learning algorithms

The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through ...

FedCAda: Adaptive Client-Side Optimization for Accelerated ... - arXiv

Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients ...

How Valuable Is Your Data? Optimizing Client Recruitment in ...

However, most federated learning algorithms require upfront commit- ... In practical settings, client recruitment can thus limit the cost of federated learning ...

Federated Learning Optimization Algorithm for Automatic Weight ...

To further lower the training cost, the augmentation FedAwo ∗ algorithm is proposed. The FedAwo ∗ algorithm takes into account the heterogeneity ...

Reviewing Federated Learning Aggregation Algorithms - MDPI

In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of ...

A systematic review of federated learning from clients' perspective

Federated learning (FL) is a machine learning approach that decentralizes data and its processing by allowing clients to train intermediate ...

Federated learning process of client-server architecture [17].

from publication: Federated Learning Algorithms to Optimize the Client and Cost ... False positive rate. Federated Learning Algorithms to Optimize the Client and ...

Optimization Algorithms for Heterogeneous Clients in Federated ...

Sateyn Kale (Google Research) Federated Learning has emerged as an important paradigm in modern large-scale machine learning, ...

Faster Rates for Compressed Federated Learning with Client ...

Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted ...

A Federated-Learning Algorithm Based on Client Sampling ... - MDPI

... costs, therefore enhancing the fairness of the federated-learning algorithm ... Addressing the fairness issue in federated learning helps improve the ...

FedChain: Chained Algorithms for Near-optimal Communication ...

Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many ...