In the domain of machine learning (ML), the conventional process typically involves the transfer and storage of data within centralised data centres or on cloud platforms. However, privacy remains a paramount concern due to the inherently sensitive and confidential nature of most data. As a result, many organisations today struggled with data isolation issues. 

To tackle these challenges, a federated learning platform – FedPro, has been developed for better AI training to facilitate cross-institutional and cross-country collaboration. It eliminates the need to transmit raw data, thus preserving privacy and ensuring compliance with regulatory standards. Furthermore, FedPro empowers users of all skill levels to explore and deploy federated learning applications with little to no coding expertise, making it suitable for a wide array of applications such as Healthcare, FinTech, Manufacturing, IoT, and Supply Chain.

While most of the current frameworks, platforms, and tools for training FL models are based on Centralised Federated Learning (CFL), in which a central server collects model parameters from multiple clients and executes the aggregate, this centralisation poses certain challenges such as a single point of failure and communication bottlenecks. FedPro addressed these issue by introducing a novel decentralised model aggregation at numerous clients, which greatly reduces reliance on a single central server and improving decentralisation. This ground-breaking design could potentially results in significant improvements in scalability, resilience, and overall efficiency.

Managing heterogeneous data distribution from different parties could lead to reduced accuracy and longer convergence times. FedPro develops an advanced weighted-average aggregation method to achieve high performance model aggregation, even when dealing with skewed data distributions. Additionally, FedPro integrates communication efficient multi-party computation techniques to ensure secure sharing of model parameters during the model aggregation process and implements contribution-aware client selection to prevent participants from benefitting without contributing their data, all while safeguarding the privacy of the raw data. 

FedPro can be further developed into a suite of IPs in privacy-preserving computing to enhance trust in digital healthcare, and be applied across various industry domain such as Healthcare,  FinTech, Smart Nation and Manufacture, etc.