# Components ## 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.flock.io/flock-products/fl-alliance/components.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.