Federated Learning Agent Transform for Secure Model Updates
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Solution Overview
Problem
Federated learning systems face challenges in secure communication between agent entities and a centralized parameter server due to the potential interception of model updates by eavesdroppers, leading to latency and overhead issues when using encrypted messages.
Innovation Solution
Implementing a method where agent entities apply a secret key-defined transform to their computational results before sending them to the server entity, which applies the inverse transform to process the results, ensuring secure communication without increasing latency or overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encrypted messages are used to protect model updates during transmission, then data privacy is improved, but communication latency and system overhead increase
Solution Approach 1:
The patent extracts the encryption operation from the communication path by having agents apply a secret key-defined transform to their model updates before transmission. The server applies the inverse transform to recover the original updates, eliminating the need for traditional encryption/decryption operations during communication and thereby reducing latency while maintaining security.
Solution Approach 2:
Instead of the server encrypting messages before sending to agents or decrypting incoming messages from agents (traditional approach), the patent inverts the approach: agents encrypt their updates using a secret key before sending, and the server decrypts using the inverse transform. This inversion shifts the computational burden to the edge devices and eliminates server-side decryption latency.
2Reliability
If encrypted messages are used to protect model updates, then data privacy is improved, but system overhead increases
Solution Approach 1:
The patent extracts the computationally expensive encryption/decryption operations from the system by replacing them with lighter secret key-based transforms at the agent side and corresponding inverse transforms at the server side. This extraction reduces the overall system overhead while maintaining the security function.
Solution Approach 2:
The patent uses disposable secret keys that are inexpensive to generate and manage. These secret keys enable simple transform operations rather than complex encryption algorithms, reducing computational overhead. The keys are used temporarily for each communication round and then discarded, minimizing long-term security management complexity.
Data Source
AI summary
There is provided mechanisms for performing an iterative learning process with agent entities. A method is performed by a server entity. The method comprises associating a secret key with a computational task. The secret key defines a transform to be used by the agent entities when reporting computational results of the computational task to the server entity. The transform has an inverse. The method comprises configuring the agent entities with the computational task. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met. The server entity as part of performing the iterative learning process applies the inverse of the transform to the computational results received from the agent entities.