Machine learning made secure / fast / efficient
Empowering Collaboration
Data-driven industries such as finance face a fundamental challenge: the need to leverage highly valuable, private data for advanced predictive modeling while complying with strict data privacy regulations. Federated Learning (FL) offers a promising solution but remains limited by the constraints of traditional federated algorithms, which can hinder performance and make it difficult to balance privacy with the need for collaborative insights.
Federated Learning (FL) is a distributed approach to machine learning that enables different organizations, devices, or entities to collaboratively train a model without exposing or sharing their underlying data. This method allows organizations to retain data locally and train models onsite, while only exchanging model parameters with the other peers in the network, all while preserving privacy and compliance with regulations like GDPR and HIPAA. However, traditional FL methods often require a centralized server for coordination, making them susceptible to various limitations, including high network demands, data biases, and difficulties in handling non-IID data distributions.
Gemsen offers a powerful alternative to traditional federated learning, providing unbreakable privacy preservation alongside the ability to easily explore and model data across multiple parties. Data stays local and is never shared. The GEM, a powerful abstraction that captures essential mathematical relationships within the dataset, is completely privacy preserving - it is impossible to reverse engineer them back to the underlying data.
The GEMs are shared with the other parties and merged as desired, enabling each party to independently explore and analyze the entire ‘dataset’. This approach means Gemsen provides the federation members the best aspects of collaboration: independent research with shared data that maintains privacy and compliance.