Dynamic Clustering in Federated Learning

Kim, Yeongwoo; Håkim, Ezeddin Al; Haraldson, Johan; Eriksson, Henrik; Silva, José Mairton B. da; Fischione, Carlo

doi:10.1109/icc42927.2021.9500877

Cited by 33 publications

(14 citation statements)

References 11 publications

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“…The AdaCFL algorithm [36] exploits the implicit connection between local models and data distributions to adaptively group clients into the optimal number of clusters. A generative adversarial network (GAN)based method was proposed in [37] to perform adaptive client clustering in multi-task FL.…”

Section: A Enhanced Federated Learning Algorithmsmentioning

confidence: 99%

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Duan¹,

Huang²,

Hu³

et al. 2022

Preprint

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Full leverage of the huge volume of data generated on a large number of user devices for providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to developing UI lies in the involvement of the large number of network devices, which contribute their data to collaborative Machine Learning (ML) and provide their computational resources to support the learning process. Federated Learning (FL) is a new ML method that enables data owners to collaborate in model training without exposing private data, which allows user devices to contribute their data to developing UI. Edge computing deploys cloud-like capabilities at the network edge, which enables network devices to offer their computational resources for supporting FL. Therefore, a combination of FL and edge computing may greatly facilitate the development of ubiquitous intelligence. In this article, we present a comprehensive survey of the recent developments in technologies for combining FL and edge computing with a holistic vision across the fields of FL and edge computing. We conduct our survey from both the perspective of an FL framework deployed in an edge computing environment (FL in Edge) and the perspective of an edge computing system providing a platform for supporting FL (Edge for FL). From the FL in Edge perspective, we first identify the main challenges to FL in edge computing and then survey the representative technical strategies for addressing the challenges. From the Edge for FL perspective, we first analyze the key requirements for edge computing to support FL and then review the recent advances in edge computing technologies that may be exploited to meet the requirements. Then we discuss open problems and identify some possible directions for future research on combining FL and edge computing, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field.

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Section: A Enhanced Federated Learning Algorithmsmentioning

confidence: 99%

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Duan¹,

Huang²,

Hu³

et al. 2022

Preprint

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show abstract

“…• Federated Dynamic Clustering [28]: A clustered federated learning scheme that utilizes ClusterGAN to assign cluster indices to clients, performs cluster calibration, and executes cluster division to update the number of clusters. • Dynamic-Fusion Federated Learning (DF FL) [46]: An extension on the FedAvg algorithm that selects participating clients based on local training time at each communication round and performs aggregation based on client model performance and server waiting time.…”

Section: ) Hyperparameter Selectionmentioning

confidence: 99%

“…Ouyang et al [27] proposed to use an indicator matrix and update it via alternating optimization during training to cluster clients. Kim et al [28] also developed an algorithm to estimate the optimum number of clusters required for FL.…”

Section: Introductionmentioning

confidence: 99%

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Low-overhead Clustered Federated Learning for Personalized Stress Monitoring

Jiang¹,

Firouzi²,

Chakrabarty³

2022

Preprint

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Advances in Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) are enabling remote health monitoring in everyday settings for early detection and prevention of chronic health problems. Such solutions can be used to augment a conventional physician-centered healthcare system. Stress, as one of the critical health problems, affects individuals adversely in terms of both physical and mental health. Prior studies on stress evaluation utilize a centralized cloud-based approach that combines data from each client for modeling. However, such a centralized approach raises data privacy concerns. To preserve privacy, decentralized federated learning has been proposed as a potential alternative framework. Nevertheless, existing federated learning algorithms have to deal with data heterogeneity; data skewness in each participant can significantly degrade the overall model performance. To tackle this challenge, we present a personalized, low-overhead clustered federated learning algorithm for stress-level recognition. The proposed algorithm outperforms two state-of-the-art baseline algorithms by providing over 7% and 12% increase in accuracy, respectively. We also present a novel cold-start algorithm for new clients who join the trained system. Our results suggest that this cold-start algorithm is robust in terms of individual classification accuracy and total training time.

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“…In an FL system, the clustering problem can be categorized into client clustering and data clustering. Motivated by heterogeneous data distribution across clients and the needs for training personalized models [5,6,7], client clustering algorithms are developed to partition the clients into different clusters to participate in training of different models [8,9,10,11]. On the other hand, data clustering problem aims to obtain a partition of data points distributed on FL clients, such that some clustering loss is minimized.…”

Section: Introductionmentioning

confidence: 99%

Secure Federated Clustering

Li¹,

Hou²,

Buyukates³

et al. 2022

Preprint

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We consider a foundational unsupervised learning task of k-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achieves 1) universal performance: no performance loss compared with clustering over centralized data, regardless of data distribution across clients; 2) data privacy: each client's private data and the cluster centers are not leaked to other clients and the server. In SecFC, the clients perform Lagrange encoding on their local data and share the coded data in an information-theoretically private manner; then leveraging the algebraic structure of the coding, the FL network exactly executes the Lloyd's k-means heuristic over the coded data to obtain the final clustering. Experiment results on synthetic and real datasets demonstrate the universally superior performance of SecFC for different data distributions across clients, and its computational practicality for various combinations of system parameters. Finally, we propose an extension of SecFC to further provide membership privacy for all data points.

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Dynamic Clustering in Federated Learning

Cited by 33 publications

References 11 publications

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Low-overhead Clustered Federated Learning for Personalized Stress Monitoring

Secure Federated Clustering

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