FedSim: Similarity guided model aggregation for Federated Learning

Palihawadana, Chamath; Wiratunga, Nirmalie; Wijekoon, Anjana; Kalutarage, Harsha

doi:10.1016/j.neucom.2021.08.141

Cited by 51 publications

(9 citation statements)

References 26 publications

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“…Due to the multiple local updates and consistency between the targeted distribution, the authors' proposed unbiased gradient variant [60] aggregation method was used, and using this method, faster convergence and higher accuracy were achieved. The author of [72] proposed a FedSim aggregation model for local and global aggregation. In this method, at first, similar gradients were clustered using local aggregation and then globally aggregated for better convergence and variance.…”

Section: Aggregation Methodsmentioning

confidence: 99%

“…Aggregation:The main goal of the aggregation method is to help increase the learning rate of the global model from distributed clients. Reduction in communication rounds, multi-centre aggregation [64], conditions for aggregation [61], robust schemes for aggregation [63,65], matching with similar clients prior to aggregation [68], aggregation with similar models [72], asynchronous aggregation [160], Effective aggregation rules on the server side [161], and energy consumption during aggregation are some of the pertinent future directions to be considered with respect to aggregation design in federated learning.…”

Section: Challenges and Future Direction Of Research In Design Of Fed...mentioning

confidence: 99%

See 1 more Smart Citation

Federated Learning Design and FunctionalModels: Survey

John

Utomo

Rouniyar

et al. 2022

Preprint

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Federated learning is a multiple device collaboration setup designed tosolve machine learning problems under framework for aggregation andknowledge transfer in distributed local data. This distributed modelensures the privacy of data at each local node. Owing to its relevance,there has been extensive research activities and outcomes in federatedlearning with expanded applicability to different areas by the researchcommunity. As such, there is a vast research archive made available by thecommunity with research work and articles related to the various aspectsof federated learning such as applications, challenges, privacy, function-alities, and design. With respect to the function and design of federatedlearning, client selection, aggregation, knowledge transfer, managementof distributed data (Non-IID), Incentive of data and communication costare of paramount importance. Any effective design of federated learningrequires these aspects to be well considered.There are numerous surveyarticles found among the available literature that focus on its applica-tion and challenges, opportunities, data privacy and protection, as wellas on federated learning on internet of things, federated learning on edgecomputing, etc.1 In this paper, a review of the available literature on the various ele-ments of design and functionalities in federated learning has been carriedout with an aim to lay emphasis on the important challenges andresearch opportunities. More specifically, this work has endeavored tounderstand and summarize the various functional methods available,along with their techniques and goals. Additionally, it has strived toget a bird’s eye view of how various functions and designs of feder-ated learning have been used in applications, and how it has helpeduncover challenges and promising research directions for the future.

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Section: Aggregation Methodsmentioning

confidence: 99%

Section: Challenges and Future Direction Of Research In Design Of Fed...mentioning

confidence: 99%

Federated Learning Design and FunctionalModels: Survey

John

Utomo

Rouniyar

et al. 2022

Preprint

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“…Federated learning is a distributed machine learning approach in which clients learn global models in a privacypreserving way, taking into account both information sharing and privacy protection [28]. e research on federal learning in cross-border e-commerce open innovation is very rare and still blank.…”

Section: Realization Mode Of Open Innovation Enterprise Wishing To Ga...mentioning

confidence: 99%

“…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. Lee and Lee [32] aggregated the strategies of each system into central strategies to speed up learning.…”

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

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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.

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“…It can solve the problem of data silos very well. In the current mainstream federated averaging algorithm [4] (FAVG), an ML model is trained locally with the user's private data, and then the model parameters are uploaded. The central server aggregates the uploaded parameters and updates the global model.…”

Section: Introductionmentioning

confidence: 99%

PPEFL: An Edge Federated Learning Architecture with Privacy-Preserving Mechanism

Liu

Gao

Wang

et al. 2022

Wireless Communications and Mobile Computing

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The emergence of federal learning makes up for some shortcomings of machine learning, and its distributed machine learning paradigm can effectively solve the problem of data islands, allowing users to collaboratively model without sharing data. Clients only need to train locally and upload model parameters. However, the computational power and resources of local users are frequently restricted, and ML consumes a large amount of computer resources and generates enormous communication consumption. Edge computing is characterized by low latency and low bandwidth, which makes it possible to offload complicated computing tasks from mobile devices and to execute them by the edge server. This paper is dedicated to reducing the communication cost of federation learning, improving the communication efficiency, and providing some privacy protection for it. An edge federation learning architecture with a privacy protection mechanism is proposed, which is named PPEFL. Through the cooperation of the cloud server, the edge server, and the edge device, there are two stages: the edge device and the edge server cooperate to complete the training and update of the local model, perform several lightweight local aggregations at the edge server, and upload to the cloud server and the cloud server aggregates the uploaded parameters and updates the global model until the model converges. The experimental results show that the architecture has good performance in terms of model accuracy and communication consumption and can well protect the privacy of edge federated learning.

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FedSim: Similarity guided model aggregation for Federated Learning

Cited by 51 publications

References 26 publications

Federated Learning Design and FunctionalModels: Survey

Federated Learning Design and FunctionalModels: Survey

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

PPEFL: An Edge Federated Learning Architecture with Privacy-Preserving Mechanism

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