Improving Federated Learning Personalization via Model Agnostic Meta Learning (1909.12488v2)

Abstract: Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data created on mobile phones by the activity of their users. Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually. In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number of similarities with the objective of personalization for FL. We present FL as a natural source of practical applications for MAML algorithms, and make the following observations. 1) The popular FL algorithm, Federated Averaging, can be interpreted as a meta learning algorithm. 2) Careful fine-tuning can yield a global model with higher accuracy, which is at the same time easier to personalize. However, solely optimizing for the global model accuracy yields a weaker personalization result. 3) A model trained using a standard datacenter optimization method is much harder to personalize, compared to one trained using Federated Averaging, supporting the first claim. These results raise new questions for FL, MAML, and broader ML research.

• The paper introduces a novel meta-learning view of FedAvg, framing it within the MAML context to improve personalization in federated learning.
• It proposes a two-stage approach that uses initial FedAvg training followed by Reptile-based fine-tuning with adaptive optimization to enhance personalized performance.
• Experimental results on EMNIST-62 demonstrate that the proposed method achieves higher personalized accuracy and rapid convergence on non-i.i.d. datasets.

Improving Federated Learning Personalization via Model Agnostic Meta Learning

This paper presents a compelling exploration of the intersection between Federated Learning (FL) and Model Agnostic Meta Learning (MAML), with a focus on optimizing personalization in FL systems. The authors investigate how principles of MAML can enhance the objective of personalizing global models in FL, which is critical given the decentralized and heterogeneous nature of data across devices, such as mobile phones.

Key Contributions

1. Interpreting FedAvg as a Meta Learning Algorithm: The paper proposes an innovative interpretation of the Federated Averaging (FedAvg) algorithm as a meta-learning framework. By conceptualizing FL as a meta-training process akin to MAML, the authors align FedAvg with Reptile, another MAML algorithm, highlighting the shared focus on optimizing adaptation to varied tasks.
2. Personalization Objectives: The authors delineate three primary objectives for improving personalization in FL:
   • Improved Personalized Model: Most clients should benefit from enhanced personalized performance.
   • Solid Initial Model: Ensure robustness even when local data is sparse.
   • Fast Convergence: Achieve a high-quality model swiftly.
3. Novel Algorithmic Proposal: A two-stage approach is proposed for FL personalization:
   • Initial Training with FedAvg to optimize personalized performance.
   • Fine-Tuning with Reptile and an adaptive optimizer such as Adam to enhance the initial model's stability and personalization capability.

Experimental Results

The empirical results on the EMNIST-62 dataset reveal significant insights:

• The personalized accuracy consistently surpasses the initial model's accuracy, indicating successful adaptation to non-i.i.d. datasets.
• A crucial observation is that models with similar global performance can exhibit varied capacities for personalization. The transition from FedAvg to a Reptile-based fine-tuning reinforces the model’s ability to adapt locally, with tangible improvements in personalized accuracy.

The experiments quantify these improvements, highlighting that increasing local epochs (E) enhances personalized performance, albeit with a potential trade-off in the stability of the initial model.

Implications and Future Directions

This research advances both theoretical and practical applications:

• Practical Deployment: The proposed algorithms offer a pathway to more robust and efficient personalization in real-world FL deployments, crucial for user-specific applications like predictive text input.
• Theoretical Insights: The connection between FL and MAML raises important questions about the fundamental objectives of FL. The traditional focus on global model accuracy is challenged, suggesting a nuanced perspective that prioritizes personalized adaptation.
• Research Directions: Future studies could explore optimizing adaptive capacities directly, possibly uncovering new optimization techniques or architectures. Moreover, cross-pollination of ideas between FL and MAML invites broader methodological innovations.

In closing, the paper underscores the potential for symbiotic integration of meta-learning concepts into federated frameworks, suggesting a promising avenue for enhancing personalization without sacrificing model stability or efficiency. This intersection poses intriguing research questions and practical implementations poised to influence the trajectory of distributed learning systems.