MONAI (Medical Open Network for AI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive suite of modular components for building, training, and deploying medical image analysis models, including segmentation, classification, detection, registration, reconstruction, and generative tasks. The architecture is library-oriented, with well-separated subpackages for transforms, networks, losses, metrics, handlers, engines, data I/O, and domain-specific applications (pathology, reconstruction, auto3dseg, federated learning). Key design patterns include composable and invertible transforms (with lazy evaluation support), a config-driven bundle system for reproducible workflows, Ignite-based training engines with reusable event handlers, and a MetaTensor data structure that carries spatial metadata through transformations. The codebase is extensively tested (over 1500 test files) and includes C++/CUDA extensions for performance-critical operations. While primarily a software library, MONAI also serves as a research platform, incorporating state-of-the-art architectures (UNETR, SwinUNETR, diffusion models, HyenaMixer) and supporting automated pipeline generation (Auto3DSeg) and federated learning. Its hybrid nature combines production-grade engineering with research-oriented flexibility.
PySyft is a privacy-preserving data science platform that enables data owners to share private datasets and approve code execution without exposing raw data. The codebase is a monorepo with multiple interdependent packages: `syft` (sync engine, peer management, encryption), `syft-rds` (high-level remote data science client for datasets and jobs), `syft-job` (job lifecycle, runner, disclosures), `syft-datasets` (dataset storage with versioned protocol codecs), `syft-enclave` (confidential computing with TEE attestation via Confidential Spaces or Tinfoil, signed receipts), `syft-bg` (background services for auto-approval, email notifications, systemd management, TUI), `syft-permissions` (declarative ACL engine), `syft-restrict` (static code verification and obfuscation for private regions), `syft-migration` (generic versioned migration framework for objects), `syft-notebook-ui` (rich HTML representation for Jupyter), and `syft-perms` (file-level permission browser). The core workflow: data scientists submit Python jobs encapsulating analysis code → data owners approve (manually or automatically) → jobs run in sandboxed environments or TEE enclaves → results and logs are staged and released according to multi-party disclosure policies. The system uses Google Drive as the default sync transport with offline-first P2P file exchange, protocol versioning for backward compatibility, and cryptographic key pinning for trust.
This repository is a curated collection of machine unlearning research, combining a comprehensive survey paper (in the form of a Kaggle notebook) with executable implementations of several unlearning algorithms across different domains. It includes code for graph neural network unlearning (GraphProjector), federated learning unlearning (FedEraser), and approximate nearest neighbor search with unlearning (UnfoldedSelf-ReconstructionHashing, a fork of ann-benchmarks). The repo serves both as a literature index (taxonomy, evaluation metrics, datasets) and a benchmark hub for reproducing and comparing unlearning methods. While the survey is actively maintained, the algorithm codebases are research prototypes with varying levels of polish and portability.
This repository is a curated 'awesome list' for Federated Learning (FL) research, maintained as a Jekyll static site. It aggregates hundreds of linked GitHub repositories and DBLP search strategies. The core data asset is a machine-generated go.mod file that pseudo-pins the linked repositories as Go dependencies, produced by the add_dependent.py script which extracts GitHub URLs from README.md, queries the GitHub API for commit hashes, and writes a go.mod. However, this is not a functional Go module; it is a novelty lockfile. The dblp-search-keywords.md document provides a meticulous analysis of DBLP search syntax to discover FL papers without the 'federate' keyword. The repository has significant hybrid character: while it is primarily a content aggregation site (application-adjacent), its technical substance lies in the data exploration and search strategy documentation (research-oriented). The actual code is minimal and tightly coupled to the repository's specific structure, limiting reusability. The add_dependent.py script is a one-time scraper with hardcoded rate limiting and token placeholder, not a general-purpose tool.