FedPerf: A Practitioners’ Guide to Performance of Federated Learning Algorithms

Mulay, Ajinkya; Gaspard, Baye; Naidu, R. China Appala; Gonzalez-Toral, Santiago; Vineeth, S; Semwal, Tushar; Agrawal, Ayush

doi:10.31219/osf.io/q3vkt

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…This is especially useful when Federated Learning is used in wireless sensor nodes as network communication is expensive in terms of energy consumption. Other techniques have reduced the computational cost for the individual nodes [41]- [43].…”

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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Section: Retraining and Personalizing Modelsmentioning

confidence: 99%

TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

Leroux¹,

Simoens²,

Lootus³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For further discussion and advanced settings on federated learning, we refer to [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ].…”

Section: Federated Machine Learningmentioning

confidence: 99%

Federated Quantum Machine Learning

Chen

Yoo

2021

Entropy

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Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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FedPerf: A Practitioners’ Guide to Performance of Federated Learning Algorithms

Cited by 2 publications

References 8 publications

TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

Federated Quantum Machine Learning

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