DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information
This work addresses convergence issues in decentralized federated learning for collaborative machine learning on heterogeneous devices, representing an incremental improvement.
The paper tackled data and model heterogeneity in decentralized federated learning by introducing a novel aggregation approach that uses second-order information to generate consensus weights, resulting in strong generalizability of local models at reduced communication costs.
Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange model information for learning a generalized model. However, variations in individual experiences and different levels of device interactions lead to data and model initialization heterogeneities across devices. Such heterogeneities leave variations in local model parameters across devices that leads to slower convergence. This paper tackles the data and model heterogeneity by explicitly addressing the parameter level varying evidential credence across local models. A novel aggregation approach is introduced that captures these parameter variations in local models and performs robust aggregation of neighbourhood local updates. Specifically, consensus weights are generated via approximation of second-order information of local models on their local datasets. These weights are utilized to scale neighbourhood updates before aggregating them into global neighbourhood representation. In extensive experiments with computer vision tasks, the proposed approach shows strong generalizability of local models at reduced communication costs.