SharifAbuadbba / split-learning-1D
Data61' CSIRO Distributed System Security Group. We have developed this algorithm to explore the question - Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training? The work has been accepted in asiaccs 2020
☆12Updated 7 months ago
Related projects: ⓘ
- Code for the paper "Bayesian Differential Privacy for Machine Learning"☆22Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆52Updated last year
- Federated k-means clustering algorithm implementation and proof of concept.☆26Updated 3 years ago
- Code for Paper "Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"☆30Updated last year
- SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things☆99Updated 3 years ago
- Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).☆13Updated 3 years ago
- ☆17Updated 3 years ago
- Integration of SplitNN for vertically partitioned data with OpenMined's PySyft☆26Updated 4 years ago
- Decentralized federated learning of deep neural networks on non-iid data☆37Updated 2 years ago
- This is official code for ACIIDS2022 paper "Meta-learning and Personalization layer in Federated learning"☆26Updated 11 months ago
- Differentially Private Federated Learning on Heterogeneous Data☆56Updated 2 years ago
- ☆35Updated 4 months ago
- Federated Learning and Membership Inference Attacks experiments on CIFAR10☆18Updated 4 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆37Updated 5 months ago
- Simplicial-FL to manage client device heterogeneity in Federated Learning☆21Updated last year
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆37Updated 2 years ago
- Local Differential Privacy for Federated Learning☆16Updated last year
- Adaptive gradient sparsification for efficient federated learning: an online learning approach☆17Updated 3 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆66Updated 3 years ago
- A summay of existing works on vertical federated/split learning☆15Updated 2 years ago
- Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection, backed by FedML, Inc.☆45Updated 2 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆24Updated last month
- reveal the vulnerabilities of SplitNN☆30Updated 2 years ago
- Implementation of Shuffled Model of Differential Privacy in Federated Learning." AISTATS, 2021.☆17Updated last year
- Federated learning is a distributed learning method that trains a deep network on user devices without collecting data from central serve…☆12Updated 4 years ago
- FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs☆15Updated last year
- Eluding Secure Aggregation in Federated Learning via Model Inconsistency☆11Updated last year
- code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"☆31Updated 2 years ago
- An Efficient Learning Framework For Federated XGBoostUsing Secret Sharing And Distributed Optimization☆26Updated 2 years ago