EVALUATION OF PARAMETER-OPTIMIZATION TECHNIQUES IN AN INDUSTRIAL FEDERATED LEARNING SYSTEM

EVALUATION OF PARAMETER-OPTIMIZATION TECHNIQUES IN AN INDUSTRIAL FEDERATED LEARNING SYSTEM

Registration ID: IJNRD_200197

Published ID: IJNRD2307074

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VIDHYA S , KULANDAIVEL P

Industrial federated learning, Optimization approaches, Hyperparameter optimization

Federated Learning (FL) decouples model train- ing from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. The performance of a machine learning algorithm is highly sensitive to the choice of its hyperparameters. In an FL setting, hyperparameter optimization poses new challenges. In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system. In an effort to reduce communication costs, a critical bottleneck in FL, we investigated a local hyperparameter optimization approach that – in contrast to a global hyperparameter optimization approach – allows every client to have its own hyperparameter configuration. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set using an i.i.d. partition and on an Internet of Things (IoT) sensor based industrial data set using a non-i.i.d. partition.

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"EVALUATION OF PARAMETER-OPTIMIZATION TECHNIQUES IN AN INDUSTRIAL FEDERATED LEARNING SYSTEM", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 7, page no.a583-a588, July-2023, Available :https://ijnrd.org/papers/IJNRD2307074.pdf

Volume 8 Issue 7, July-2023

Pages : a583-a588

Paper Reg. ID: IJNRD_200197

Published Paper Id: IJNRD2307074

Downloads: 000121161

Research Area: Science

Country: Erode, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307074.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2307074

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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