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Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions
Machine Learning · Submitted 5 Jul 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance.
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The abstract, sentence by sentence
The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. | background 0.98 | 0.05 | 0.02 | asserted 1.00 |
| s2 | To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. | background 1.00 | 0.04 | 0.03 | asserted 0.98 |
| s3 | The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. | background 0.87 | 0.08 | 0.03 | asserted 0.94 |
| s4 | Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. | background 0.57 | 0.04 | 0.03 | asserted 0.99 |
| s5 | Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. | background 0.76 | 0.06 | 0.03 | speculative 0.76 |
| s6 | To this, we are first to introduce the idea of personalization in federated clustering. | contribution 0.99 | 0.84 | 0.09 | asserted 1.00 |
| s7 | The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. | contribution 0.79 | 0.19 | 0.13 | asserted 0.88 |
| s8 | We propose p-FClus that addresses these goal in a single round of communication between server and clients. | contribution 0.64 | 0.91 | 0.18 | asserted 1.00 |
| s9 | We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance. | result 0.87 | 0.94 | 0.70 | asserted 1.00 |
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Original abstract
The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance.
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Cites as background 6 papers
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Similarly, ( [ref] ) investigates federated data clustering in Gaussian Mixture Models (GMM) and Expectation Maximation (EM) algorithms, respectively.
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms · Related Work · 0.93
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Other Works: Recent studies have investigated federated data clustering in the soft (or better called fuzzy) clustering paradigm, wherein a data point can have membership to more than a single cluster ( [ref] ).
Towards Federated Clustering: A Federated Fuzzy $c$-Means Algorithm (FFCM) · Related Work · 0.99
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Some works in this direction approach the problem by framing it as a generative data synthesis challenge and leveraging concepts from Generative Adversarial Networks (GANs) ( [ref] ).
Federated clustering with GAN-based data synthesis · Related Work · 0.97
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