Components

This sub-section introduces the three components comprising our FL Alliance.

1. FLocKit

• Encapsulates task-specific training logic

• Offers predefined templates for various tasks, such as LLM fine-tuning, time series prediction, and more

• Provides an effortless way to create a federated learning (FL) task (for instance, to create a LLM finetuning FL task, users only need to specify the target model they want to fine-tune in the configuration file, without requiring any changes to the source code)

2. FL Client

FL Client serves as both an organiser and an orchestrator, facilitating the interaction between smart contracts and federated learning (FL) training tasks. It also manages all necessary system-level environments, ensuring seamless operation and coordination throughout the process. Specifically, FL Client:

• Interacts with smart contracts

• Allows proposers to train their local models using their own local data

• Allows voters to submit scores based on the performance of the proposers

• Aggregates scores submitted by voters

3. Smart Contracts

• Assigns roles to participants randomly

• Allows participants to stake and withdraw rewards

• Slashes malicious behaviours