Federated Learning
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
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.
Executive Impact & Key Metrics
As data becomes increasingly distributed, sensitive, and voluminous, conventional centralized Artificial Intelligence (AI) pipelines are no longer practical today. Federated Learning (FL) and Collaborative Learning (CL) techniques developed over the last decade are crucial. OmniFed, a Python-based modular, extensible, configurable, and open-source framework, enables FL/CL from the edge to High Performance Computing (HPC) systems. Built with layered abstractions and clear separation of concerns, OmniFed works in a plug-and-play and override-what-you-need manner via lifecycle hooks. It supports rapid prototyping of new FL/CL algorithms without excessive boilerplate, streamlining FL deployment across heterogeneous environments. This framework allows researchers to focus on design and innovation rather than infrastructure and setup complexities.
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Configurable FL Algorithms
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GPUs Used in Testing
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Peak Model Accuracy (ResNet18)
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Pages of Research
Deep Analysis & Enterprise Applications
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Challenges & Related Work
Design Philosophy & Core Components
Configuration & Deployment
This section delves into the evolving landscape of AI/ML systems, highlighting the limitations of centralized approaches and the critical need for federated learning. It reviews existing frameworks like TensorFlow Federated, NVFLARE, Flower, OpenFL, MONAI, PySyft, FedML, APPFL, and IBM FL, discussing their strengths and weaknesses in terms of scalability, privacy, and deployment complexity. OmniFed aims to address these limitations by offering a more flexible and modular solution.
OmniFed is designed with modularity, flexibility, and extensibility as first-class citizens in the FL/CL ecosystem. It uses precise, layered abstractions, separating local computation, communication, and algorithmic control. Key components include the Engine for orchestration, Topology for node graph definition, Node for participant roles (client, aggregator, relay), Communicator for data exchange (supporting gRPC, MPI, MQTT), and Algorithm for training logic. It integrates privacy-preserving techniques like DP, HE, and SA.
OmniFed utilizes Hydra for YAML-based configuration, enabling easy customization of algorithms, topologies, communicators, and privacy features. It supports quick deployment using Ray, handling distributed workloads across heterogeneous hardware. This section demonstrates how users can easily switch between algorithms like FedAvg and FedProx, configure communication with compression, simulate streaming data, and incorporate privacy mechanisms with minimal code changes.
Enterprise Process Flow
Local Data Training (Client)
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Model Updates Sent (Client)
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Aggregation (Server/Aggregator)
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Global Model Update (Server)
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New Model Distributed (Client)
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Configurable FL Algorithms Out-of-the-Box
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Cross-Facility FL with Mixed Protocols
OmniFed's modular design enables complex cross-facility federated learning scenarios. This involves multiple geographically distributed sites collaborating on a shared model. Within a site, nodes can leverage high-bandwidth MPI collectives for efficient aggregation, acting as an inner communicator. Across sites, gRPC can manage slower, high-latency networks as an outer communicator. This flexible approach allows for optimized data exchange tailored to network characteristics, ensuring both speed and robust communication across diverse environments.
Advanced ROI Calculator
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Implementation Roadmap
Our structured approach ensures a smooth integration of advanced AI solutions into your existing enterprise architecture.
Phase 1: Discovery & Strategy
In-depth analysis of your current infrastructure, data landscape, and business objectives. Development of a tailored FL/CL strategy aligned with your organizational goals and privacy requirements.
Phase 2: Pilot & Proof-of-Concept
Deployment of a small-scale pilot project using OmniFed to demonstrate technical feasibility and measure initial performance. Iterative refinement based on feedback and results.
Phase 3: Scaled Integration
Full-scale integration of OmniFed within your enterprise, including custom topology configuration, integration with diverse data sources, and deployment across edge to HPC environments.
Phase 4: Optimization & Monitoring
Continuous monitoring of model performance, communication overhead, and privacy mechanisms. Ongoing optimization to maximize efficiency and ROI, ensuring long-term success.
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