Federated learning is a machine learning technique that trains an algorithm across multiple decentralized data sources, without the need to centralize or share the data. This enables privacy-preserving and efficient model development, as well as tapping into the raw data streaming from various devices and sensors. Federated learning has applications in various industries, such as healthcare, telecommunications, defence, and finance.
What is Federated Learning?
In traditional machine learning, data are collected and merged into one central server, where a model is trained on the aggregated data. This approach has several drawbacks, such as:
- Data privacy and security risks: Centralizing data exposes them to potential breaches, leaks, or misuse by unauthorized parties. It also requires obtaining consent from data owners and complying with data protection regulations.
- Data transfer and storage costs: Moving large amounts of data across the network consumes bandwidth and time. Storing and processing data in the cloud also incurs costs.
- Data heterogeneity and quality issues: Data from different sources may have different formats, distributions, or quality levels. This may affect the performance and generalization of the model.
Keeping Data Local
Federated learning addresses these challenges by keeping the data localized at their sources, such as mobile phones, laptops, or private servers. Instead of sending the data to a central server, federated learning sends the model parameters (e.g., the weights and biases of a neural network) to the data sources. Each data source then trains a local model on its own data and sends back the updated parameters to the central server. The server then aggregates the parameters from all sources and updates the global model. This process is repeated until the model converges.
By doing so, federated learning achieves several advantages, such as:
- Data privacy and security preservation: Data are never shared or exposed to anyone else. Only encrypted parameters are exchanged between the sources and the server.
- Data transfer and storage reduction: Only a small number of parameters are transferred across the network, instead of the entire datasets. Data are also stored locally at their sources, reducing cloud storage costs.
- Data heterogeneity and quality handling: Data sources can train local models that are tailored to their own data characteristics and quality. The global model can then benefit from the diversity and richness of the local models.
How Federated Learning Can Be Used by Fintech Companies Offering Trade Finance
Trade finance is a form of financing that facilitates international trade transactions between buyers and sellers. It involves various intermediaries, such as banks, insurers, exporters, importers, logistics providers, etc. Trade finance requires comprehensive credit analysis and risk assessment of the parties involved in a transaction, as well as verification of trade documents and contracts.
Federated learning can be used by fintech companies offering trade finance to improve their credit scoring and risk management capabilities, while preserving the privacy and security of their clients’ data. For example:
- Fintech companies can collaborate with banks and other financial institutions to train a federated learning model on their respective credit data, without sharing or exposing the data to each other. This can enhance the accuracy and robustness of the credit scoring model, as well as reduce the reliance on external credit bureaus.
- Fintech companies can also collaborate with exporters, importers, logistics providers, and other trade participants to train a federated learning model on their respective trade data, such as invoices, bills of lading, customs declarations, etc. This can improve the efficiency and reliability of the trade document verification process, as well as detect fraud and anomalies in trade transactions.
- Fintech companies can leverage federated learning to tap into the raw data streaming from various sensors and devices that monitor the trade goods’ location, condition, quality, etc. This can provide real-time visibility and traceability of the trade goods’ movement and status, as well as reduce losses and damages.
By using federated learning for trade finance, fintech companies can offer more competitive and innovative services to their clients, while ensuring their data privacy and security. Federated learning can also enable fintech companies to comply with data protection regulations in different jurisdictions, as well as foster trust and collaboration among different trade stakeholders.
In Conclusion
Federated learning is a novel machine learning technique that trains an algorithm across multiple decentralized data sources without sharing or centralizing the data. It offers several benefits over traditional machine learning techniques such as preserving data privacy and security reducing data transfer and storage costs handling data heterogeneity and quality issues tapping into raw data streaming from various devices and sensors.
Federated learning has applications in various industries such as healthcare, telecommunications, defence and finance. In particular federated learning can be used by fintech companies offering trade finance to improve their credit scoring, risk management, document verification and traceability capabilities while ensuring their clients’ data privacy and security. Federated learning can also enable fintech companies to comply with data protection regulations in different jurisdictions as well as foster trust and collaboration among different trade stakeholders
