Multi-Agent System for Federated Learning 面向联邦学习的多智能体系统
An end-to-end federated learning agent system that transforms natural language requirements into executable training workflows, covering dataset upload, task planning, code generation, secure execution, and result verification.
面向联邦学习场景构建端到端智能体系统，将自然语言需求转化为可执行训练流程，覆盖数据集上传、任务规划、代码生成、安全执行与结果验证的全流程闭环。
Workflow联邦学习智能体工作流
Requirement Understanding
Interpret user intent, task goals, and federated learning constraints.
理解用户意图、任务目标与联邦学习约束条件。
Task Planning
Decompose the request into data handling, training setup, and aggregation strategy tasks.
将请求拆解为数据处理、训练配置与聚合策略等子任务。
Code Generation
Generate executable federated learning code with knowledge-enhanced context.
结合知识增强上下文生成可执行的联邦学习代码。
Secure Execution
Run generated code in isolated containers with runtime monitoring and result return.
在隔离容器中安全执行代码，并完成运行监控与结果回传。
Review & Verification
Audit outputs, validate results, and close the loop with actionable feedback.
审查输出结果、完成验证，并通过反馈形成闭环优化。
Core Capabilities of the Federated Learning Agent联邦学习智能体核心能力
Planner-Executor-Reviewer Collaboration
Build a hierarchical multi-agent framework that automatically decomposes federated learning requirements into executable stages such as data preparation, training configuration, aggregation strategy design, execution, and review.
基于 Planner-Executor-Reviewer 构建分层协作框架，将联邦学习需求自动拆解为数据准备、训练配置、聚合策略设计、执行与审查等可执行阶段。
LangGraph Workflow Orchestration
Use LangGraph and a state-machine driven workflow to manage task states, node context, and intermediate artifacts, with support for rollback, retry, recovery, and result auditing across long federated training chains.
基于 LangGraph 与状态机工作流统一管理任务状态、节点上下文和中间产物，支持失败回滚、异常重试、流程恢复与结果审查。
RAG-Powered Federated Knowledge Engine
Combine RAG with PostgreSQL to retrieve training templates, algorithm configurations, parameter constraints, and historical task experience, improving the agent’s understanding of federated learning terminology, workflows, and code generation accuracy.
基于 RAG + PostgreSQL 构建联邦学习知识增强模块，对训练模板、算法配置、参数约束与历史任务经验进行检索增强，提升术语理解与代码生成准确率。
Secure Containerized Execution
Run agent-generated code inside isolated Docker containers with non-intrusive runtime injection for log capture, GPU memory monitoring, metric reporting, and result return, ensuring stronger execution safety, observability, and system isolation.
设计容器化任务隔离与非侵入式运行时注入机制，使生成代码在受控 Docker 容器中安全执行，并支持日志采集、显存监控、指标上报与结果回传。
Autonomous Toolchain with Memory
Integrate Tool Calling, Memory, and multi-agent collaboration to encapsulate dataset management, task generation, code execution, and result analysis into a reusable toolchain, enabling context-aware planning, autonomous tool selection, and multi-round decision making.
基于 Tool Calling、Memory 与多智能体协作机制封装数据集管理、任务生成、代码执行与结果分析工具链，支持智能体结合上下文自主选取工具并进行多轮决策。
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Try our other Federated Learning project / 先试试我们的另一个联邦学习项目 ↗