OmniFed统一配置与通信，让联邦学习在边缘到超算间灵活部署。
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
- 模块化设计解耦配置、通信与训练逻辑，支持按需定制。
- 支持混合通信协议与多种隐私保护机制，提升部署灵活性。
- 适合需要跨设备、跨平台部署联邦学习的研究者与工程师。
联邦学习（FL）在数据分散且注重隐私的边缘计算与高性能计算（HPC）场景中至关重要。本文提出OmniFed，一个围绕解耦与职责分离设计的模块化框架，支持配置驱动的原型设计和代码级按需覆盖定制。框架支持不同拓扑结构、单次部署中的混合通信协议以及主流训练算法，并提供可选的隐私保护机制，包括差分隐私（DP）、同态加密（HE）和安全聚合（SA），以及压缩策略。这些能力通过明确定义的扩展点暴露，用户可自定义拓扑、编排、学习逻辑及隐私/压缩插件，同时保障核心系统完整性。我们评估了多种模型与算法在多个性能指标下的表现。通过统一拓扑配置、混合协议通信与可插拔模块，OmniFed实现了异构环境中联邦学习部署的简化。GitHub仓库地址：https://github.com/at-aaims/OmniFed。
原文摘要 · Abstract (English)
Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.
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