A two-phase half-async method for heterogeneity-aware federated learning

Ma, Tianyi; Mao, Bingcheng; Chen, Ming

doi:10.1016/j.neucom.2021.08.146

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some techniques can be implemented either at the client side or the server side, like a personalized approach that, for instance, can be implemented at the server side by keeping a record for each client to provide a personalized model [83] or at the client side where each client can have their personalized trained model locally and share a general model [87]. An adaptive approach also can be performed on the server side, where the work in [48] proves that using a fixed batch size can degrade the model performance since the data distribution and size differ between clients, so it proposes a batch adaption technique to determine the suitable batch size for each client; [99] proposes an adaptive local epoch technique to avoid overfitting the model, by decreasing the local epoch value after a certain iteration based on the global model performance. The adaptive approach can be implemented on the client side, where in [49,95], the clients adapt their learning rate based on the global model received.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Based on Federated Learning Federated learning, a distributed machine learning paradigm, allows training models of scattered data on largescale edge or mobile devices without the need to collect raw data [30], which effectively mitigates unnecessary bandwidth loss, and enhances data privacy and legitimizing [31]. Palihawadana et al [28] performed local clustering for customers with similar gradients, and then conducted further global aggregation.…”

Section: Intelligent Optimization Model Of Cross-border E-commerce Op...mentioning

confidence: 99%

Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

Zhang

Guo

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Product development and innovation is the key issue for cross-border e-commerce operation. It is of great importance to build an intelligent open innovation system and maximize its proximity to market demand with the internationalization and digital advantages of cross-border e-commerce. Cross border e-commerce data is widely distributed among each node enterprise in the supply chain. But the enterprises will not share private data for intelligent learning for the sake of data security, which has become a difficulty problem in intelligent decision-making of open innovation. This paper analyzes the research status of open innovation and the technical basis of federated learning, builds an open innovation intelligent decision-making model of cross-border e-commerce based on federated learning, trains and tests the model using data from two participants, compares the intelligent prediction effects among whole learning, local learning and federated learning, and puts forward the collaborative promotion strategy of open innovation and intelligent optimization of cross-border e-commerce based on federated learning.

show abstract

A two-stage federated optimization algorithm for privacy computing in Internet of Things

Zhang

Ning

Xue

2023

Future Generation Computer Systems

View full text Add to dashboard Cite

A two-phase half-async method for heterogeneity-aware federated learning

Cited by 4 publications

References 9 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

Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

A two-stage federated optimization algorithm for privacy computing in Internet of Things

Contact Info

Product

Resources

About