An Efficient Framework for Clustered Federated Learning (2006.04088v2)

Abstract: We address the problem of federated learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their own objectives (learning tasks) but by aggregating their data with others in the same cluster (same learning task), they can leverage the strength in numbers in order to perform more efficient federated learning. For this new framework of clustered federated learning, we propose the Iterative Federated Clustering Algorithm (IFCA), which alternately estimates the cluster identities of the users and optimizes model parameters for the user clusters via gradient descent. We analyze the convergence rate of this algorithm first in a linear model with squared loss and then for generic strongly convex and smooth loss functions. We show that in both settings, with good initialization, IFCA is guaranteed to converge, and discuss the optimality of the statistical error rate. In particular, for the linear model with two clusters, we can guarantee that our algorithm converges as long as the initialization is slightly better than random. When the clustering structure is ambiguous, we propose to train the models by combining IFCA with the weight sharing technique in multi-task learning. In the experiments, we show that our algorithm can succeed even if we relax the requirements on initialization with random initialization and multiple restarts. We also present experimental results showing that our algorithm is efficient in non-convex problems such as neural networks. We demonstrate the benefits of IFCA over the baselines on several clustered FL benchmarks.

• The paper introduces IFCA, which iteratively refines cluster assignments and optimizes models using gradient descent to enhance personalization.
• The paper provides theoretical convergence guarantees for linear models with squared loss and strongly convex conditions under federated settings.
• The paper validates IFCA on datasets like Rotated MNIST and Federated EMNIST, demonstrating superior clustering and model performance over baselines.

An Efficient Framework for Clustered Federated Learning

The paper presents a novel approach to federated learning (FL) by addressing the challenge of data heterogeneity through a concept termed Clustered Federated Learning (CFL). In CFL, users are organized into clusters where each group shares similar learning tasks, allowing for more effective collaboration and model personalization. The authors introduce the Iterative Federated Clustering Algorithm (IFCA), which aims to iteratively refine cluster assignments and optimize model parameters using gradient descent.

Key Contributions

1. IFCA Algorithm: The paper introduces IFCA, which alternates between estimating user cluster identities and optimizing models via gradient descent. This iterative approach is designed to address the non-trivial issue of unknown cluster identities in a decentralized environment.
2. Theoretical Analysis: The authors provide a comprehensive convergence analysis of the algorithm. They prove that IFCA converges under certain conditions, particularly in linear models with squared loss and strongly convex smooth loss functions. For instance, they demonstrate that in linear models with two clusters, convergence is guaranteed if initialization is slightly better than random.
3. Weight Sharing in Ambiguous Clusters: For scenarios with ambiguous clustering structures, the authors propose combining IFCA with weight sharing techniques. This approach is particularly useful for deep learning models, where shared layers can capture global patterns, and cluster-specific layers are tuned for personalized tasks.
4. Experimental Validation: The paper presents experiments on both synthetic and real-world datasets like Rotated MNIST and Federated EMNIST. IFCA demonstrates superior performance compared to baseline methods, effectively identifying clusters and improving model accuracy in non-convex problems, such as neural networks.

Numerical Results & Analysis

IFCA shows robust performance across various configurations. For example, when applied to the Federated EMNIST dataset, it outperforms both global and local model approaches, indicating its efficacy in leveraging cluster-specific information without centralized clustering, thus reducing computational overhead at the server. The method proves resilient to random initializations, further underscoring its practical viability.

Implications and Future Directions

The framework proposed in this paper has significant implications for federated learning systems, especially where data is inherently non-i.i.d. or clustered. By improving personalized model accuracy while reducing centralized computation requirements, IFCA offers an efficient and scalable solution for real-world FL applications.

The paper opens several avenues for future research, such as extending theoretical guarantees to weakly convex or fully non-convex loss functions. Further investigation into stochastic gradients and limited device participation could enhance the algorithm's applicability and robustness in more challenging federated environments.

Overall, this research contributes a thoughtful and well-substantiated algorithmic framework for advancing personalized learning within federated systems.