HERL (Homomorphic Encryption Reinforcement Learning): A Reinforcement Learning-based Approach that Uses Q-Learning to Dynamically Optimize Encryption Parameters

Federated Learning (FL) is a technique that allows Machine Learning models to be trained on decentralized data sources while preserving privacy. This method is especially helpful in industries like healthcare and finance, where privacy issues prevent data from being centralized. However, there are big problems when trying to include Homomorphic Encryption (HE) to protect the privacy of the data while it’s being trained. 

Homomorphic Encryption protects privacy by enabling computations on encrypted data without requiring its decryption. However, it does come with significant computational and communication overheads, which can be particularly troublesome in settings where clients have disparate processing capacities and security needs. The environment for using HE in FL is challenging due to the wide range of client needs and capabilities. 

For example, some clients may have less processing capacity and less urgent security needs, while others may have strong computing resources and strict security requirements. In such a diverse environment, implementing one encryption method might result in inefficiencies, causing some clients to endure needless delays and others not to receive the requisite degree of protection.

As a solution, a team of researchers has introduced Homomorphic Encryption Reinforcement Learning (HERL), a Reinforcement Learning-based technique. With the help of Q-Learning, HERL dynamically optimizes the encryption parameter selection to meet the unique requirements of various client groups. It optimizes two primary encryption parameters: the coefficient modulus and the polynomial modulus degree. These parameters are important because they have a direct impact on the encryption process’s computational load and security level.

The first step in the procedure is to profile the customers according to their security needs and computing capabilities, including memory, CPU power, and network bandwidth. A clustering approach is used to classify clients into tiers based on this profiling. The HERL agent then steps in, dynamically choosing the best encryption settings for every tier after the clients have been tier-by-tiered. This dynamic selection is made possible by Q-Learning, in which the agent gains knowledge from the environment by experimenting with various parameter settings and then uses that knowledge to make the best decisions possible by striking a balance between security, computing efficiency, and utility.

Upon experimentation, the team has shared that HERL demonstrated that it can boost convergence efficiency by up to 30%, decrease the time needed for the FL model to converge by up to 24%, and improve utility by up to 17%. Since these advantages are attained with little security sacrifice, HERL is a reliable option for integrating HE in FL across a variety of client settings.

The team has summarized their primary contributions as follows.

1. A reinforcement learning (RL) agent-based technique has been presented to choose the best homomorphic encryption settings for dynamic federated learning. Since this method is generic, it can be used with any FL clustering scheme. The RL agent adjusts to each client’s unique requirements to provide FL systems with the best possible balance between security and performance.

2. The suggested approach provides a more successful security, utility, and latency trade-off. Through adaptive design, the system reduces computing overhead while preserving the necessary degree of FL data security. This enhances FL operations’ efficiency without risking the confidentiality of the client’s data.

3. The results have shown a notable improvement in training efficiency, up to a 24% increase in performance. 

The study has also tackled a number of important issues to back up these contributions, including the following.

1. The effects of HE parameters on FL performance and the best ways to use HE in FL applications have been studied.

2. It has been examined how FL’s varied client environments can be accommodated by expanding the clustering mechanism.
3. This optimization focuses on finding the best way to combine security, computational overhead, and usefulness in FL with HE.

4. It has been analyzed how well RL works at adjusting HE parameters dynamically for various client tiers.

5. It has been assessed if using an RL-based approach improves overall FL system performance and trade-off.

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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.