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Mergers and acquisitions come with new data from the partner company.Data governance rules such as GDPR may require that data be kept in specific geolocations and specify retention and privacy policies.There are many reasons a single company might rely on multiple data storage solutions. Intra-company use case: Leverage siloed internal data Edge computing: Learning across thousands of edge devices.Inter-company: Facilitating collaboration between organizations.Intra-company: Bridging internal data silos.This is where federated learning comes in.įederated learning enables companies to leverage new data resources without requiring data sharing.īroadly, three types of use cases are enabled by federated learning: Many organizations could improve current AI models by incorporating new datasets that cannot be easily accessed without sacrificing privacy. Furthermore, the real-world performance of your ML model depends not only on the amount of data but also the relevance of the training data. The wave of data privacy legislation being enacted worldwide today (starting with GDPR in Europe and CCPA in California, with many similar laws coming soon) will only accelerate the need for privacy-preserving ML techniques in all industries.Įxpect federated learning to become an essential part of the AI toolset in the years ahead. Privacy concerns, however, aren’t limited to financial data. Company-wide ROI can increase as businesses gather all viable data for new products, including recommender systems, fraud detection systems, and call center analytics. In financial institutions, we see an incredible opportunity for federated learning to bridge internal data silos. However, federated learning is not just about collaborating with external partners. Large-scale collaborations in healthcare have demonstrated the real-world viability of using federated learning for multiple independent parties to jointly train an AI model. Advantages of privacy-preserving technology Note that these local sites could be servers, edge devices like smartphones, or any machine that can train locally and send back the model updates to the central server. This global model can capture the insights from the entire dataset, even when actual data cannot be combined. This preserves data privacy and sovereignty.įinally, the central server collects all the updates from each site and intelligently aggregates the “mini-models” into one global model. This is the key feature of federated learning: only the model updates or parameters are shared, not the training data itself. Each site trains the model locally on its subset of the data, and then sends only the model parameters back to a central server. The approach involves creating multiple versions of the model and sending one to each server or device where the datasets live. The distributed training datasets are instead left where they are. On the other hand, federated learning does not assume that one unified dataset can be created. This leaves many datasets and use-cases off-limits for applying AI techniques. However, this approach is not feasible for much of the world’s data that is sensitive. Today’s standard ML approach requires first gathering all the training data in one place. What is federated learningįederated learning is a ML technique that enables the extraction of insights from multiple isolated datasets-without needing to share or move that data into a central repository or server.įor example, assume you have multiple datasets you want to use to train an AI model. We present three ways federated learning can be used in financial services and provide tips on getting started today. This post introduces federated learning and explains its benefits for businesses handling sensitive datasets. How can companies in the financial services industry leverage their own data while ensuring privacy and security? Massive internal datasets that would be valuable for training ML models remain unused. More practically, it ignores data egress challenges and the considerable cost of creating large pooled datasets. This is an unrealistic assumption when dealing with data sovereignty and security considerations or sensitive data like personally identifiable information. For instance, traditional ML methods assume all data can be moved to a central repository.

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Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML).












Trex coloring page