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Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their security posture, they must alleviate cybersecurity risks. Federated learning might be able to do both.
What is federated learning?
Federated learning is an approach to AI development in which multiple parties train a single model separately. Each downloads the current primary algorithm from a central cloud server. They train their configuration independently on local servers, uploading it upon completion. This way, they can share data remotely without exposing raw data or model parameters.
The centralized algorithm weighs the number of samples it receives from each disparately trained configuration, aggregating them to create a single global model. All information remains on each participant's local servers or devices -- the centralized repository weighs the updates instead of processing raw data.
Federated learning's popularity is rapidly increasing because it addresses common development-related security concerns. It is also highly sought after for its performance advantages. Research shows this technique can improve an image classification model's accuracy by up to 20% -- a substantial increase.
Horizontal federated learning
There are two types of federated learning. The conventional option is horizontal federated learning. In this approach, data is partitioned across various devices. The datasets share feature spaces but have different samples. This enables edge nodes to collaboratively train a machine learning (ML) model without sharing information.
Vertical federated learning
In vertical federated learning, the opposite is true -- features differ, but samples are the same. Features are distributed vertically across participants, each possessing different attributes about the same set of entities. Since just one party has access to the complete set of sample labels, this approach preserves privacy.
How federated learning strengthens cybersecurity
Traditional development is prone to security gaps. Although algorithms must have expansive, relevant datasets to maintain accuracy, involving multiple departments or vendors creates openings for threat actors. They can exploit the lack of visibility and broad attack surface to inject bias, conduct prompt engineering or exfiltrate sensitive training data.
When algorithms are deployed in c ...