Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

Jin, Yilun; Wei, Xiguang; Liu, Yang; Yang, Qiang

doi:10.48550/arxiv.2002.11545

Cited by 22 publications

(22 citation statements)

References 32 publications

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“…However, these methods were primarily designed for clustered datasets, and hence such adaptations may not be suitable if the labeled data and unlabeled data come from different distributions. Still, the few federated learning works that address label deficiency [20]- [22], demonstrate the feasibility and value of exploiting unlabeled data from multiple sources.…”

Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Kassem¹,

Alapatt²,

Mascagni³

et al. 2022

Preprint

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Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning taskspecific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain.

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Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Kassem¹,

Alapatt²,

Mascagni³

et al. 2022

Preprint

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“…A recent study [16] has questioned the soundness of the assumption that devices have well-annotated labels in a federated setting. Existing semi-supervised federated learning (SSFL) approaches, such as FedMatch [15] and FedSemi [26], have only recently started to be examined under the vision domain to exploit unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

Federated Self-training for Semi-supervised Audio Recognition

Tsouvalas

Saeed

Özçelebi

2022

ACM Trans. Embed. Comput. Syst.

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Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices like smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose FedSTAR to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13.28% compared to the fully-supervised federated model.

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“…Here, the individual nodes might operate without human intervention or feedback which means that the data remains completely unlabeled. Even if the user can be asked to provide a label, most likely, this will be of low quality [45]. Several techniques have been developed that can use unlabelled local data to improve the global model either in a semi-supervised [44], [46]- [48] or unsupervised way [49], [50] .…”

Section: Retraining and Personalizing Modelsmentioning

confidence: 99%

TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

Leroux¹,

Simoens²,

Lootus³

et al. 2022

Preprint

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Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges. Most works focus on the limited computational resources of edge platforms but this is not the only bottleneck standing in the way of widespread adoption. In this paper we list several other challenges that a TinyML practitioner might need to consider when operationalizing an application on edge devices. We focus on tasks such as monitoring and managing the application, common functionality for a MLOps platform, and show how they are complicated by the distributed nature of edge deployment. We also discuss issues that are unique to edge applications such as protecting a model's intellectual property and verifying its integrity.

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Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

Cited by 22 publications

References 32 publications

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Federated Self-training for Semi-supervised Audio Recognition

TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

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