Computer Science > Machine Learning
arXiv:2409.02064 (cs)
[Submitted on 3 Sep 2024 (v1), last revised 8 Sep 2024 (this version, v2)]
Title:Personalized Federated Learning via Active Sampling
View a PDF of the paper titled Personalized Federated Learning via Active Sampling, by Alexander Jung and Yasmin SarcheshmehPour and Amirhossein Mohammadi
View PDFAbstract:Consider a collection of data generators which could represent, e.g., humans equipped with a smart-phone or wearables. We want to train a personalized (or tailored) model for each data generator even if they provide only small local datasets. The available local datasets might fail to provide sufficient statistical power to train high-dimensional models (such as deep neural networks) effectively. One possible solution is to identify similar data generators and pool their local datasets to obtain a sufficiently large training set. This paper proposes a novel method for sequentially identifying similar (or relevant) data generators. Our method is similar in spirit to active sampling methods but does not require exchange of raw data. Indeed, our method evaluates the relevance of a data generator by evaluating the effect of a gradient step using its local dataset. This evaluation can be performed in a privacy-friendly fashion without sharing raw data. We extend this method to non-parametric models by a suitable generalization of the gradient step to update a hypothesis using the local dataset provided by a data generator.
| Subjects: | Machine Learning (cs.LG) |
| MSC classes: | 68T05 |
| ACM classes: | I.2.6; I.2.11 |
| Cite as: | arXiv:2409.02064 [cs.LG] |
| (orarXiv:2409.02064v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2409.02064 arXiv-issued DOI via DataCite |
Submission history
From: Alexander Jung [view email][v1] Tue, 3 Sep 2024 17:12:21 UTC (573 KB)
[v2] Sun, 8 Sep 2024 08:29:34 UTC (572 KB)
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View a PDF of the paper titled Personalized Federated Learning via Active Sampling, by Alexander Jung and Yasmin SarcheshmehPour and Amirhossein Mohammadi
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