An open framework for Federated Learning.
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Updated
Jun 6, 2024 - Jupyter Notebook
An open framework for Federated Learning.
The code for the paper "Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards" AAAI'22 Oral Presentation.
Official implementation of our work "Collaborative Fairness in Federated Learning."
CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
A Collaborative Learning platform: cataloging and remixing of Open Educational Resources (OER), e-mentoring and e-tutoring, Learning Analytics (LA), and more.
Official PyTorch Implementation for DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis - CVPR 2022
An SDK for multi-agent collaborative perception.
a collaborative collection of interview questions collected from both sides of the game: Interviewer(s) and Interviewee.
[AAAI-24] TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
Fedstellar: A Platform for Decentralized Federated Learning
Virtual classroom for teaching Python 3 programming language
Explore and contribute to a diverse collection of LeetCode solutions, neatly categorized and collaboratively refined.
Um repositório de código aberto para desenvolvedores compartilharem conhecimento e experiência. Vamos aprender e crescer juntos! 🚀
Apuntes de AET curso 23/24 ULL
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
Collaborative Machine Learning approach to train a mode that classifies a person as smoker or non-smoker based on the user data. The distributed approach of training is done with secure model transmissions to central cloud location where Amazon EC2 instance aggregates the new model based on new training received in Homomorphically Encrypted forms
Changemakers want to solve big social problems. Changemappers help them find & support each other and long-lasting impactful systemic change.
Welcome to the "Harnessing the Power of AI" workshop! This GitHub repository serves as a comprehensive resource for participants seeking hands-on learning and in-depth understanding of AI concepts, techniques, and tools.
Collaborative compiler with support for Pair Programming
Decentralized and Privacy-Preserving Machine Learning: Exploring the Power of Federated Learning.
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