DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.

An SDK for multi-agent collaborative perception.

[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

a collaborative collection of interview questions collected from both sides of the game: Interviewer(s) and Interviewee.

Secure collaborative training and inference for XGBoost.

Official PyTorch Implementation for DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis - CVPR 2022

NEBULA: A Platform for Decentralized Federated Learning

Official implementation of our work "Collaborative Fairness in Federated Learning."

Learning to Walk with Dual Agents for Knowledge Graph Reasoning (AAAI'22)

[ICLR'25] Official Implementation of STAMP: Scalable Task And Model-agnostic Collaborative Perception

Collective Knowledge repository with actions to unify the access to different predictive analytics engines (scipy, R, DNN) from software, command line and web-services via CK JSON API:

Expert Systems With Applications - Camera clustering for scalable stream-based active distillation

Fedstellar: A Platform for Decentralized Federated Learning

Official implementation of FedGAT: Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction (https://arxiv.org/abs/2502.04521)

A Collaborative Learning platform: cataloging and remixing of Open Educational Resources (OER), e-mentoring and e-tutoring, Learning Analytics (LA), and more.

Simple and efficient way to learn a new programming language.

[AAAI-24] TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients

Human-Agent Collaborative Deep Reinforcement Learning

Multi-agent reinforcement learning framework for Unity environments. Implements MAPPO, MASAC, MATD3, and MADDPG with comprehensive evaluation tools. Features sample-efficient training, competitive analysis, and pre-trained models achieving great performance in Tennis and Soccer environments.