The Challenges and Vulnerabilities of Federated Learning: Insights and Solutions
Federated learning enables collaborative AI model training while preserving data privacy, yet it remains vulnerable to poisoning attacks from malicious clients. In this guest blog, Dr. Fang highlights critical vulnerabilities in federated learning frameworks and introduces innovative defenses to counter these threats, such as excluding harmful local models that degrade global performance. With a focus on overcoming challenges like poisoning attacks and straggler delays, Scalytics provides robust, regulation-compliant solutions, empowering enterprises to adopt secure and scalable AI frameworks.
