Comparative Evaluation of Clustered Federated Learning Methods (2410.14212v2)

Abstract: Over recent years, Federated Learning (FL) has proven to be one of the most promising methods of distributed learning which preserves data privacy. As the method evolved and was confronted to various real-world scenarios, new challenges have emerged. One such challenge is the presence of highly heterogeneous (often referred as non-IID) data distributions among participants of the FL protocol. A popular solution to this hurdle is Clustered Federated Learning (CFL), which aims to partition clients into groups where the distribution are homogeneous. In the literature, state-of-the-art CFL algorithms are often tested using a few cases of data heterogeneities, without systematically justifying the choices. Further, the taxonomy used for differentiating the different heterogeneity scenarios is not always straightforward. In this paper, we explore the performance of two state-of-theart CFL algorithms with respect to a proposed taxonomy of data heterogeneities in federated learning (FL). We work with three image classification datasets and analyze the resulting clusters against the heterogeneity classes using extrinsic clustering metrics. Our objective is to provide a clearer understanding of the relationship between CFL performances and data heterogeneity scenarios.

• The paper compares two clustered federated learning algorithms, demonstrating that server-side clustering effectively handles non-IID data challenges.
• It categorizes data heterogeneity into five scenarios, providing a structured framework for analyzing federated learning performance.
• Experiments on MNIST, Fashion-MNIST, and KMNIST reveal that tailored clustering significantly improves model convergence and accuracy.

Comparative Evaluation of Clustered Federated Learning Methods

The paper presents an in-depth evaluation of Clustered Federated Learning (CFL) strategies with respect to data heterogeneity in federated learning (FL) systems. This research primarily investigates two state-of-the-art CFL algorithms, targeting the challenge introduced by non-IID data distributions among federated clients.

Federated Learning and Data Heterogeneity

Federated Learning is a decentralized machine learning framework designed to enhance data privacy by training models locally on client devices without sharing raw data. A significant obstacle in FL systems is the presence of non-IID (non-independent and identically distributed) data, which can hinder algorithmic convergence and model performance. To address this, CFL divides clients into clusters where their data distributions are homogeneous, thus enabling tailored model updates.

Data Heterogeneity Taxonomy

The paper constructs a taxonomy to categorize client data heterogeneity into five distinct scenarios:

1. Concept Shift on Features: Identical labels but different features among clients.
2. Concept Shift on Labels: Same features with differing labels.
3. Feature Distribution Skew: Varied features across clients, with consistent label distributions.
4. Label Distribution Skew: Uneven label distributions despite homogeneous feature distributions.
5. Quantity Skew: Significant variation in data volume between clients.

This systematic categorization permits a structured exploration of CFL efficiency across different types of heterogeneity.

Comparative Analysis of CFL Algorithms

The authors evaluate two prominent CFL approaches: Server-side Clustering and Client-side Clustering. Server-side Clustering relies on the central server for deducing clustering based on model weights, whereas Client-side Clustering assigns cluster responsibility and model selection directly to the clients.

Experimental Setup

The experiments incorporate three image classification datasets—MNIST, Fashion-MNIST, and KMNIST—to simulate various non-IID scenarios, systematically exploring the impact of specific heterogeneities on CFL performance.

Key Findings

• Server-side Clustering generally achieved optimal separation of clients per heterogeneity class, resulting in performance improvements closely matching benchmarks set by centralized oracle models.
• Client-side Clustering displayed variability, with performance improvements noted in scenarios with clear heterogeneity classes such as "concept shift on features." However, it struggled in settings where initial random clustering led to suboptimal results.
• Features and labels heavily influence cluster efficacy, with substantial challenges observed in "feature distribution skew" and "quantity skew" scenarios wherein typical CFL solutions may not yield significant benefits.

Implications and Future Directions

This paper underscores the criticality of personalized learning in federated settings, emphasizing improved performance when data heterogeneity is effectively harnessed through clustering. It also highlights the necessity for prior knowledge of client data distribution types to optimize model performance effectively.

Moving forward, research could focus on automating cluster determination without predefined heterogeneity knowledge and extending the scalability of these methods to more complex datasets. Additionally, exploring hybrid models that blend client-side insights with server-side computation might present avenues for enhancing CFL resilience against diverse data patterns.

The comparative analysis provided here serves as a foundational understanding of CFL efficacy across structured heterogeneity scenarios and encourages further exploration into refining federated learning systems to handle varied, real-world data distributions robustly.