Federated Meta-Learning with Fast Convergence and Efficient Communication (1802.07876v2)

Abstract: Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.

• The paper introduces FedMeta, which integrates meta-learning with federated learning to achieve faster convergence and higher accuracy compared to traditional FedAvg.
• The paper leverages methods like MAML and Meta-SGD to adapt quickly to non-IID, personalized data, cutting communication rounds by up to 4.33 times and boosting accuracy by up to 14.84%.
• The paper demonstrates practical applications for privacy-preserving, efficient model training in diverse industries such as mobile computing and personalized recommendations.

Federated Meta-Learning with Fast Convergence and Efficient Communication

In recent machine learning literature, one prominent challenge involves training models across distributed networks of mobile devices while adhering to privacy constraints. Traditional federated learning methods rely heavily on sharing a global model across devices, which poses statistical and systematic challenges due to non-IID data and disparate device capabilities. This paper introduces an innovative approach to these challenges through a federated meta-learning framework named FedMeta, which significantly diverges from previous methods by sharing a parameterized algorithm (or meta-learner) instead of a global model.

Summary of Contributions

The authors propose FedMeta to integrate meta-learning with federated learning, demonstrating significant improvements over traditional approaches. The framework is designed to handle non-IID, highly personalized data effectively, and is tested on various federated datasets alongside a real-world production dataset. FedMeta achieves two key performance improvements over the conventional Federated Averaging (FedAvg) algorithm: lowered communication costs by a factor of 2.82 to 4.33 and enhanced accuracy by 3.23% to 14.84%.

The authors present solid evidence supporting these claims through extensive empirical evaluations. The framework incorporates meta-learning methods like Model-Agnostic Meta-Learning (MAML) and Meta-SGD, which facilitate rapid adaptation to new tasks. In FedMeta, the task of training models is distributed across clients, and only the algorithm parameters are communicated back to the server for updates, effectively preserving user privacy.

Key Numerical Results

The experiments carried out on LEAF datasets and a production dataset indicate that FedMeta consistently outperforms traditional federated learning approaches in terms of accuracy and system overhead. Specifically, the framework achieves higher accuracies with a notably lower number of communication rounds and has proven capabilities of adapting quickly to new clients with minimal data. The FEMNIST dataset, for instance, experienced an accuracy boost from the usual 76.79% with FedAvg to 89.26% using FedMeta.

This is further validated in an industrial recommendation task that demonstrates FedMeta's superior performance and its ability to provide precise recommendations with reduced computational and communication resources. These results are instrumental for settings where privacy and efficiency are high-priority concerns.

Implications and Speculations

The implications of FedMeta are profound, as it offers a scalable solution for training personalized machine learning models in federated setups, applicable across various industries such as telecommunications and personalized marketing. The flexible nature of sharing algorithmic parameters rather than model parameters enhances privacy preservation and reduces the overhead cost associated with federated learning.

Looking forward, the adoption of FedMeta could lead to further exploration of meta-learning algorithms in federated settings, particularly as devices become more interconnected, and the demand for personalized services increases. This advancement presents potential for research into optimizing algorithm parameter sharing, minimizing privacy risks, and exploration into other meta-learning strategies that could further refine model generalization and adaptation capabilities.

In summary, FedMeta presents a well-evidenced, robust framework that leverages meta-learning to address pressing issues in federated learning. Its ability to achieve faster convergence with reduced communication highlights its viability for practical deployment and its potential to shape future developments in AI methodology and application.