On the Design of Communication-Efficient Federated Learning for Health Monitoring

Chu, Dong; Jaafar, Wael; Yanıkömeroğlu, Halim

doi:10.1109/globecom48099.2022.10001077

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…This technique can provide faster and more communication-efficient training in federated learning. Clustering is where similar clients are clustered together and a representative is selected to share the updated model [172,177,185]. Periodic model averaging [198,209] is where the local model is uploaded periodically to reduce the number of updates.…”

Section: Discussionmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client’s limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.

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Section: Discussionmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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“…Além disso, a divisão hierárquica do treinamento reduz o impacto causado por distribuic ¸ões de dados não-IID na convergência do modelo, pois os autores assumem que os dados possuem maior similaridade segundo a distância geográfica dos clientes. CEFL (Communication-Efficient Federated Learning) é um arcabouc ¸o para o treinamento de modelos para dados médicos através de aprendizado federado [Chu et al 2022]. Os autores propõem determinar a similaridade entre os clientes através do cálculo da distância euclidiana dos pesos das redes neurais dos clientes.…”

Section: Personalizac ¸ãO De Modelos No Aprendizado Federadounclassified

ATHENA-FL: Evitando a Heterogeneidade Estatística através do Um-contra-Todos no Aprendizado Federado

Souza

Camilo

Rebello

et al. 2023

Anais Do VII Workshop De Computação Urbana (CoUrb 2023)

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O aprendizado federado é um novo paradigma que permite o treinamento de modelos de aprendizado de máquina através da colaboração entre clientes e um servidor de agregação. O treinamento dispensa o compartilhamento de dados privados, garantindo aos clientes privacidade de suas amostras. Entretanto, quando os clientes possuem distribuições de dados distintas, o treinamento apresenta dificuldades de convergência, resultando em erros preditivos no modelo final. Este artigo propõe um sistema de aprendizado federado que considera clientes com distribuições de dados heterogêneas e, mesmo assim, produz modelos acurados em menos épocas de treinamento do que o estado da arte. Os efeitos da heterogeneidade dos dados são mitigados através do agrupamento dos clientes baseado em uma estimativa da distribuição de dados através dos pesos da rede neural treinada localmente. Além disso, o sistema utiliza a técnica um-contra-todos, treina um detector para cada classe no sistema. Assim, grupos diferentes podem combinar os detectores a fim de formar um modelo capaz de detectar classes provenientes de outros grupos. Os resultados mostram que o modelo um-contra-todos possui alta capacidade de identificar corretamente as amostras e com acurácia até 18% maior do que o treinamento tradicional, com um baixo custo de comunicação durante o treinamento, reduzindo a quantidade de bytes transmitidos entre 59,6% até 94% em comparação à arquitetura MobileNet.

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FedRx: Federated Distillation-Based Solution for Preventing Hospitals Overcrowding During Seasonal Diseases Using MEC

Sahraoui,

Kerrache,

Calafate

et al. 2024

2024 IEEE 21st Consumer Communications &Amp; Networking Conference (CCNC)

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On the Design of Communication-Efficient Federated Learning for Health Monitoring

Cited by 5 publications

References 13 publications

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

ATHENA-FL: Evitando a Heterogeneidade Estatística através do Um-contra-Todos no Aprendizado Federado

FedRx: Federated Distillation-Based Solution for Preventing Hospitals Overcrowding During Seasonal Diseases Using MEC

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