Seeking Advice on Federated Learning Frameworks, Communication, and Privacy on Edge Devices
Hi everyone,
I’m currently exploring the implementation of federated learning on edge devices to enhance privacy and data security in AI applications. This approach allows individual devices to collaboratively train a model while keeping the data localized, which is crucial for privacy-sensitive applications.
I’m facing a few challenges and would love to tap into your expertise. Specifically, I’m looking for insights on choosing the right federated learning frameworks and libraries, managing communication between devices to handle latency and data synchronization. Additionally, I’m very interested in best practices for ensuring data privacy and security in these federated environments.
I’m currently exploring the implementation of federated learning on edge devices to enhance privacy and data security in AI applications. This approach allows individual devices to collaboratively train a model while keeping the data localized, which is crucial for privacy-sensitive applications.
I’m facing a few challenges and would love to tap into your expertise. Specifically, I’m looking for insights on choosing the right federated learning frameworks and libraries, managing communication between devices to handle latency and data synchronization. Additionally, I’m very interested in best practices for ensuring data privacy and security in these federated environments.
Solution
Implementing federated learning on edge devices involves selecting frameworks like TensorFlow Federated or PySyft, managing communication for latency and data synchronization, and ensuring data privacy through techniques like differential privacy and secure aggregation. Which framework would u like to use @wafa_ath
