Three Approaches for Personalization with Applications to Federated Learning (2002.10619v2)

Abstract: The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose and analyze three approaches: user clustering, data interpolation, and model interpolation. For all three approaches, we provide learning-theoretic guarantees and efficient algorithms for which we also demonstrate the performance empirically. All of our algorithms are model-agnostic and work for any hypothesis class.

• The paper introduces three personalization approaches that tailor federated learning models to diverse client data distributions.
• It validates the methods through rigorous theoretical guarantees and empirical evaluations on synthetic and EMNIST datasets.
• The study demonstrates improved model accuracy and efficiency, with implications for applications like adaptive keyboards and predictive health systems.

Personalization Techniques in Federated Learning

This paper presents a comprehensive paper of personalization in machine learning with emphasis on federated learning (FL). The authors address the challenge of optimizing models for individual users rather than deploying a single global model, which may not perform well for clients with diverse data distributions. The work proposes three approaches: user clustering, data interpolation, and model interpolation, each evaluated both theoretically and empirically.

Proposed Approaches

1. User Clustering: This method involves partitioning clients into clusters and training a model for each group. It serves as an intermediary step between local and global models, balancing generalization and data distribution matching. The paper introduces hypothesis-based clustering, emphasizing the need for clustering algorithms that align with the specific task at hand.
2. Data Interpolation: This approach combines local client data with a global data distribution to optimize model performance. The authors draw parallels between this technique and domain adaptation, proposing a convex mixture of local and global data. The algorithm Dapper is introduced to achieve this efficiently, addressing computational constraints by strategically sampling data.
3. Model Interpolation: In this strategy, both a local and a global model are trained, and the final prediction is a weighted combination of the two. The paper presents the Mapper algorithm to optimize this process, offering a theoretical framework for model interpolation.

Theoretical Contributions

The authors provide rigorous learning-theoretic guarantees for all three personalization approaches. For user clustering, a generalization bound highlights that performance is dependent on the average, rather than the minimum, number of samples per user. Data interpolation is analyzed with convergence guarantees when the loss function is strongly convex. Lastly, model interpolation is examined through generalization bounds that ensure the method is theoretically sound.

Empirical Evaluation

Empirical results on synthetic data and the EMNIST dataset confirm the efficacy of the proposed methods. The synthetic experiments demonstrate the benefits of personalization over global models, particularly highlighting the advantages of user clustering. On the EMNIST dataset, the authors show that combining HypCluster with Dapper or Mapper provides superior model accuracy compared to baseline models, demonstrating the practical impact of their approaches.

Implications and Future Directions

The paper's findings are significant for scenarios where personalized user experiences are critical, such as virtual keyboard applications and health predictive models. The presented methods enhance the adaptability and efficiency of models while considering federated learning constraints like communication costs and privacy.

Future research may explore deeper integration of these methods with advanced optimization techniques and their applicability across different domains and hypothesis classes. Additionally, extending these personalization techniques to other areas in AI, such as recommendation systems or adaptive learning platforms, offers a promising avenue for future exploration.

In summary, this work presents robust and theoretically grounded approaches to personalization in federated learning, offering insights that bridge the gap between local adaptability and global generalization.