Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation

Artificial Intelligence (AI) technologies have revolutionized numerous fields, yet their applications often rely on costly and time-consuming data collection processes. Federated Learning (FL) offers a promising alternative by enabling AI models to be trained on decentralized data where data is scattered across clients (distributed nodes). However, existing FL approaches struggle to match the performance of centralized training due to challenges such as heterogeneous data distribution and communication delays, limiting their potential for breakthroughs. We observe that many real-world use cases involve hybrid data regimes, in which a server (center node) has access to some data while a large amount of data is distributed across associated clients. To improve the utilization of decentralized data under this regime, address data heterogeneity issue, and facilitate asynchronous communication between the server and clients, we propose a dual learning approach that leverages centralized data at the server to guide the merging of model updates from clients. Our method accommodates scenarios where server data is out-of-domain relative to decentralized client data, making it applicable to a wide range of use cases. We provide theoretical analysis demonstrating the faster convergence of our method compared to existing methods. Furthermore, experimental results across various scenarios show that our approach significantly outperforms existing technologies, highlighting its potential to unlock the value of large amounts of decentralized data.