Federated Learning (FL) is a decentralized machine learning approach that enables multiple devices to collaboratively learn a model without sharing their data, enhancing privacy and security. Security engineers and pen testers must be vigilant against unique threats such as gradient leakage, data poisoning, and model inversion attacks specific to FL systems. Adopting best practices like secure aggregation and regular security audits can help mitigate these risks. Affected: healthcare, finance, smart cities, IoT security, autonomous vehicle networks
Keypoints :
- Federated Learning (FL) allows devices to learn collaboratively without sharing sensitive data.
- Privacy is a primary concern addressed by FL, especially in sectors like healthcare and mobile computing.
- Adversarial threats such as gradient leakage, data poisoning, and model inversion are prominent in FL environments.
- Pen testing in FL involves testing for vulnerabilities associated with model updates and data integrity.
- Security measures like differential privacy and secure aggregations are critical for protecting FL systems.
- Regular security audits and proactive monitoring are essential for maintaining FL system integrity.