FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE AND SCALABLE FINANCIAL SERVICES

People are justifiably apprehensive about the safety, privacy, and scalability of their data in collaborative machine learning contexts now that digital financial services are available. But it is hard to get to, and different groups don't trust it. Blockchain is a kind of distributed ledger technology that stores and checks data in a way that doesn't need a trusted third party. However, when used alone, it has problems with its size and battery use. This research puts out a hybrid architecture that combines blockchain with FL to solve these problems and develop financial services that can grow. This approach, which uses blockchain technology, protects the data sovereignty of organisations. It also makes it easier for financial organisations to work together to build global models. It does this by being able to keep an eye on changes, build trust, and provide people the chance to get personal incentives. The suggested strategy is better than FL-only and blockchain-only solutions when it comes to accuracy, scalability, and safety. The amount of energy used and the time it takes to respond to queries are also maintained at a reasonable level. Research can perform a lot of important things with the system, including look into claims of fraud, assess credit risk, and stop money laundering. The outcomes help make the financial industry's digital infrastructure safer, more open, and more in line with the law.