jimmyc96 Goto Github PK

awesome-knowledge-distillation

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

awesome-llm-fm-wireless-communication-networking

A curation list of Awesome LLM/FM for wireless communication and networking papers, including applications, benchmarks, datasets, codes/implementations, and related resources.

dl-activesensing

Active Sensing for Communications by Learning

easyfl

An experimental platform to quickly realize and compare with popular centralized federated learning algorithms. A realization of federated learning algorithm on fairness (FedFV, Federated Learning with Fair Averaging, https://fanxlxmu.github.io/publication/ijcai2021/) was accepted by IJCAI-21 (https://www.ijcai.org/proceedings/2021/223).

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

federated-learning

Federated Learning - PyTorch

federated-learning-1

An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch

federated-learning-pytorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

federated-learning-with-differential-privacy

Implementation of dp-based federated learning framework using PyTorch

fedgrab

fedlab-benchmarks

Standard federated learning implementations in FedLab and FL benchmarks.

fedpaq-mnist-implemenation

An implementation of FedPAQ using different experimental parameters. We will be looking at different variations of how, r(number of clients to be selected), t (local epochs) and s (Quantizer levels))

fedrepo

fl-arbitrary-participation

Code for paper "A Unified Analysis of Federated Learning with Arbitrary Client Participation"

jimmyc96

Config files for my GitHub profile.

jimmyc96.github.io

Homepage

jmlr-style-file

LaTeX style file for the Journal of Machine Learning Research

llama-adapter

[ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters

llm-adapters

Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"

moon

Model-Contrastive Federated Learning (CVPR 2021)

multiobjectiveoptimization

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

neuralcollapse

Code reproducing Neural Collapse phenomenon on MSE and cross-entropy loss

niid-bench

Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)

non-iid-dataset-for-personalized-federated-learning

otfusion

Model Fusion via Optimal Transport, NeurIPS 2020

pfl-non-iid

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage of the Non-IID data to learn the personalized model for each user. Thanks to @Stonesjtu, this platform can also record the GPU memory usage for the model.

plato

A new scalable federated learning research framework

practicing-federated-learning

pytorch-cifar

95.47% on CIFAR10 with PyTorch

pytorch-distributed

A quickstart and benchmark for pytorch distributed training.