> For the complete documentation index, see [llms.txt](https://docs.flock.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flock.io/flock-products/fl-alliance/components.md). # 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 --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.flock.io/flock-products/fl-alliance/components.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.