shblong's starred repositories
Safe-Online-Learning-for-Gaussian-Processes
The code accompanies the publication "Feedback Linearization based on Gaussian Processes with event-triggered Online Learning" by Jonas Umlauft and Sandra Hirche published in IEEE Transactions on Automatic Control in 2020.
consensus-synchronization-of-MAS
consensus and synchronization algorithms of networked multi agent systems with event triggered communications
event-triggered-consensus
事件触发一致性及其对应的文献
neurips-1bCS
Code related to NeurIPS 2019 paper "Superset Technique for Approximate Recovery in One-Bit Compressed Sensing"
emoji-compress
A set of compressing and encoding algorithms that uses emoji instead of bits.
deep-gradient-compression
[ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
decentralized-byzantine-RL
Experiments code for AAMAS'24 paper on "Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence"
byzantine-gas
Official Implementation of ICML'23 "Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting".
my-pbft-simulation-py
Network simulation for PBFT (Practical Byzantine Fault Tolerance) consensus algorithm in Python
byzantine-robust-optimizer
Learning from history for Byzantine Robustness
Byzantine_AirComp
Code for our paper "Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation" (https://arxiv.org/abs/2105.10883).
Byzantine-resilient-distributed-learning
Associated codebase for Byzantine-resilient distributed / decentralized machine learning papers from INSPIRE Lab
ByzantineMomentum
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent (ICLR 2021)
rr_with_compression_experiments_source_code
Q-RR, DIANA-RR, Q-NASTYA, NASTYA-DIANA, QSGD, DIANA, FedCOM and FedPAQ on logistic loss with L2 regularization
ElegantBook
Elegant LaTeX Template for Books
ElegantPaper
Elegant LaTeX Template for Working Papers
probabilistic-federated-neural-matching
Bayesian Nonparametric Federated Learning of Neural Networks