arXiv:2407.17999v1 Announce Type: new Abstract: Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.
The article “Federated Learning for Industrial Applications: Addressing Data Heterogeneity with Lightweight Cohorting” explores the limitations of traditional federated learning (FL) in industrial settings due to data heterogeneity. While FL is widely used for collaborative learning without compromising privacy, it assumes data similarity which is not typically the case in industrial data. The authors propose a solution called Lightweight Industrial Cohorted FL (LICFL) that leverages model parameters for cohorting, allowing clients with similar data distributions to collaborate and train more specialized models. Additionally, they introduce an adaptive aggregation algorithm, Adaptive LICFL (ALICFL), to further improve the global model performance and convergence speed. Through numerical experiments on real-time data, the authors demonstrate the effectiveness of their proposed algorithms and compare their performance with existing approaches.
Federated Learning: Overcoming Data Heterogeneity in Industrial Applications
Federated Learning (FL) has gained significant popularity as a collaborative approach to decentralized Machine Learning (ML) models training. It allows clients to exchange learning without compromising data privacy. However, FL struggles to perform optimally in industrial settings due to the heterogeneity of data distributions. In this article, we introduce a novel solution called Lightweight Industrial Cohorted FL (LICFL), which overcomes the challenges posed by data heterogeneity.
The Challenge of Data Heterogeneity in Industrial Settings
Unlike homogeneous data commonly found in FL tasks, industrial data exhibits significant differences. Factors such as machine types, firmware versions, operational conditions, and environmental factors contribute to variations in data distribution. These differences hinder the effectiveness of FL, leading to degraded performance. To address this issue, we propose the LICFL algorithm.
The Lightweight Industrial Cohorted FL (LICFL) Algorithm
LICFL leverages model parameters for cohorting without the need for additional on-edge computations and communications. It enables similar clients with homogeneous data distributions to collaborate and train specialized or personalized models. By enhancing client-level model performance, LICFL mitigates the impact of data heterogeneity in industrial applications, resulting in improved overall performance.
Extending LICFL with Adaptive Aggregation
Additionally, we propose an adaptive aggregation algorithm that extends LICFL to Adaptive LICFL (ALICFL). This enhancement further improves the global model performance and speeds up convergence. By adaptively adjusting the aggregation process based on the unique characteristics of each cohort, ALICFL ensures that the global model captures the diversity of data present in industrial settings.
Numerical Experiments and Performance Comparison
To demonstrate the effectiveness of our proposed algorithms, we conducted numerical experiments on real-time industrial data. We compared the performance of LICFL and ALICFL with existing approaches. The results showcased the superior efficacy of our algorithms in mitigating the impact of data heterogeneity and achieving enhanced performance in industrial FL tasks.
Conclusion
Federated Learning has revolutionized collaborative ML training, but it faces challenges in industrial settings with heterogeneous data distributions. Our proposed LICFL and ALICFL algorithms offer innovative solutions that harness the power of model parameters and adaptive aggregation to overcome these challenges. By enhancing client-level model performance and improving the global model’s ability to capture diverse data, LICFL and ALICFL pave the way for efficient and effective FL in industrial applications.
The paper introduces a new algorithm called Lightweight Industrial Cohorted FL (LICFL) that aims to address the limitations of Federated Learning (FL) in industrial settings where data heterogeneity is common. FL is a popular approach for collaborative learning without compromising privacy by exchanging learning between clients without sharing the data. However, FL assumes data similarity or homogeneity, which is not typically the case in industrial data due to various factors such as machine type, firmware version, operational conditions, and environmental factors.
The authors highlight that FL’s performance tends to degrade when clients have heterogeneous data distributions. To address this issue, the proposed LICFL algorithm utilizes model parameters for cohorting without any additional client-level computations and communications compared to standard FL. By allowing clients with similar data distributions to collaborate, LICFL enhances client-level model performance and enables the training of more specialized or personalized models.
In addition to LICFL, the authors propose an adaptive aggregation algorithm called Adaptive LICFL (ALICFL). This algorithm further improves the global model performance and speeds up convergence. The adaptive aggregation algorithm adjusts the aggregation process based on the performance of individual clients, allowing the global model to benefit from the expertise of clients with better performance.
The efficacy of the proposed algorithms is demonstrated through numerical experiments on real-time data. By comparing the performance with existing approaches, the authors show that LICFL and ALICFL outperform traditional FL methods in industrial settings with data heterogeneity.
Overall, the paper presents a novel approach to address the challenges of FL in industrial applications. By leveraging cohorting based on model parameters and introducing adaptive aggregation, the proposed algorithms offer potential solutions to mitigate the impact of data heterogeneity and improve the performance of decentralized machine learning models. Future research could focus on evaluating the scalability and applicability of LICFL and ALICFL in larger industrial settings and exploring their performance in different types of data heterogeneity scenarios.
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