GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition

Sarkar, Abhishek; Sen, Tanmay; Roy, Ashis Kumar

doi:10.1109/icmla52953.2021.00184

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Applications of Federated Learning in HAR. While many of the early use-cases of FL were related to natural language processing [21], visual recognition [24,49], and speech recognition [22] tasks, we are now also witnessing its applications in the area of human-activity recognition [3,18,48,62,63,77]. We extend this line of work on FL in HAR, albeit in the context of multi-device environments.…”

Section: Related Workmentioning

confidence: 89%

“…This assumption comes along with the well-known drawbacks of supervised learning on sensor data, e.g., the challenges associated with data labeling. We note that unsupervised [79] and semi-supervised FL [3,62,77] are active research areas in ML and future works can investigate them in the context of multi-device systems.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Mocha [65], Ditto [42], IFCA [19], and ClusterFL [63] leverage multi-task learning to model structural relationships in distributed data and use the similarities for personalization. GraFeHTy [62] applies a Graph Convolution Network with graph representations of activity inter-relatedness. APFL [15] and MAPPER [50] perform personalization through mixing the local and global models via model interpolation.…”

Section: Related Workmentioning

confidence: 99%

“…FedDL [71] and FjORD [25] dynamically adapt model layers and model size for heterogeneous devices to participate in FL. Some studies further employ self-supervised or semi-supervised learning methods to utilize unlabeled sensor data for personalized federated learning [3,62,77]. Meta-HAR [40] adopts Model-Agnostic Meta Learning to boost the representation ability of the shared embedding network and uses personalization for adaptation on top of it.…”

Section: Related Workmentioning

confidence: 99%

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FLAME: Federated Learning Across Multi-device Environments

Cho¹,

Mathur²,

Kawsar³

2022

Preprint

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Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human-activity recognition, FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by high non-IID-ness across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy-and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.8-33.8% higher 𝐹 1 score, 1.02-2.86× greater energy efficiency, and up to 2.02× speedup in convergence to target accuracy through fair distribution of the FL workload.CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Cooperation and coordination.

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Section: Related Workmentioning

confidence: 89%

Section: Discussion and Limitationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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FLAME: Federated Learning Across Multi-device Environments

Cho¹,

Mathur²,

Kawsar³

2022

Preprint

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“…Apart from the aforementioned applications, FGML has manifold applications in other domains. A number of related papers have explored applications of FGML to various problems, such as human activity recognition [93], neural architecture search [109], packet routing [73], malware detection [20; 85], multi-armed bandits [145], drug discovery [74], and financial crime detection [99].…”

Section: Other Applicationsmentioning

confidence: 99%

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

Fu¹,

Zhang²,

Dong³

et al. 2022

Preprint

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Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many realworld scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.

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Federated Learning and Genetic Mutation for Multi-Resident Activity Recognition

Anicai,

Shakir

2023

2023 IEEE 19th International Conference on E-Science (E-Science)

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GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition

Cited by 11 publications

References 8 publications

FLAME: Federated Learning Across Multi-device Environments

FLAME: Federated Learning Across Multi-device Environments

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

Federated Learning and Genetic Mutation for Multi-Resident Activity Recognition

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