FedML

Orchestrates federated learning training across decentralized devices and servers using the Federated Averaging (FedAvg) algorithm, where model updates are aggregated server-side without exchanging raw data. Implements ServerAggregator and ClientTrainer interfaces with pluggable communication backends (MQTT, TRPC) to coordinate training rounds across heterogeneous edge devices, mobile phones, and cloud servers. Supports both synchronous and asynchronous aggregation patterns with configurable convergence criteria.

FedML Launch provides a unified scheduler that abstracts away cloud provider differences, enabling users to submit ML jobs once and execute them across AWS, Azure, GCP, or on-premise clusters without code changes. The Scheduler Layer manages resource allocation, job distribution, and execution environment provisioning by translating job specifications into provider-specific configurations. Integrates with Docker for containerized deployment and supports both batch and interactive job modes.

Integrates Docker containerization for packaging training and serving workloads with automatic image building from source code. Provides Docker deployment templates for common ML scenarios (distributed training, federated learning, model serving) that can be customized via configuration. Supports multi-stage builds for optimized image sizes and layer caching for faster iteration.

Implements MLOpsRuntimeLogDaemon for asynchronous event logging during training and inference, capturing training events, system events, and errors without blocking execution. Provides structured event format (MLOpsProfilerEvent) with timestamps and metadata for post-hoc analysis. Supports log rotation and compression to manage disk space for long-running jobs.

Provides pluggable algorithm framework with ServerAggregator and ClientTrainer interfaces enabling implementation of custom federated learning algorithms beyond FedAvg. Supports algorithm composition and chaining for complex training pipelines. Includes reference implementations (FedAvgAggregator, FedAvgTrainer) demonstrating interface contracts and best practices.

Provides simulation environment for federated learning across heterogeneous devices (servers, edge devices, mobile phones) without requiring actual hardware deployment. Simulates network latency, device failures, and data heterogeneity to validate algorithm behavior before production deployment. Supports both synchronous and asynchronous simulation modes with configurable device characteristics.

Enables large-scale distributed training of foundational models using data parallelism across multiple GPUs and nodes. Implements gradient synchronization and model parameter averaging using AllReduce collective operations, with support for mixed-precision training and gradient accumulation. Integrates with PyTorch DistributedDataParallel and TensorFlow distributed strategies to transparently distribute training across heterogeneous hardware while maintaining single-machine code semantics.

Provides high-performance model serving infrastructure for scalable inference across cloud and edge environments. Implements model loading, batching, and request routing with support for multiple model formats (ONNX, TorchScript, SavedModel). Integrates with containerization and auto-scaling to handle variable inference loads, with built-in monitoring for latency and throughput metrics.

Implements FedMLDefender component with multiple defense mechanisms against adversarial attacks in federated learning, including differential privacy, robust aggregation, and anomaly detection. Provides configurable privacy budgets and defense strategies that can be applied transparently to training pipelines without modifying algorithm code. Integrates with attack simulation framework for testing defense effectiveness.

Implements FedMLAttacker component that simulates various adversarial attacks (poisoning, model inversion, membership inference) against federated learning systems to validate defense mechanisms. Provides configurable attack strategies and intensity levels that can be injected into training pipelines for red-teaming and robustness validation. Generates detailed attack success metrics and vulnerability reports.

Implements MqttCommManager and S3 integration for reliable message-oriented communication between federated learning clients and servers, with support for asynchronous message queuing and cloud storage for model checkpoints. Uses MQTT publish-subscribe pattern for decoupled client-server communication, enabling clients to connect/disconnect without blocking aggregation. Integrates with S3-compatible storage for distributed model versioning and checkpoint management.

Provides Android SDK enabling federated learning training directly on mobile devices with on-device model updates and gradient computation. Implements lightweight ClientTrainer for Android that communicates with federated learning servers via MQTT or HTTP, with support for model quantization and compression to fit memory constraints. Handles battery and network state management to pause/resume training based on device conditions.

Implements MLOps metrics collection system (MLOpsMetrics, MLOpsProfilerEvent) that captures training performance data including loss, accuracy, throughput, communication time, and resource utilization. Provides runtime logging daemon (MLOpsRuntimeLogDaemon) that asynchronously collects metrics without blocking training, with integration to cloud monitoring platforms. Enables performance profiling and bottleneck identification across distributed training jobs.

Provides command-line interface (CLI) for job submission, model deployment, and system management with configuration file support (YAML/JSON). Implements MLOpsConfigs for centralized configuration management across training, serving, and federated learning components. Supports environment variable substitution and configuration inheritance for managing complex multi-environment deployments.