krishnap25 / simplicial-fl
Simplicial-FL to manage client device heterogeneity in Federated Learning
☆21Updated last year
Related projects: ⓘ
- ☆54Updated 3 years ago
- ☆14Updated 8 months ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆52Updated last year
- ☆40Updated 11 months ago
- Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)☆43Updated last year
- ☆13Updated last year
- Federated Learning with Partial Model Personalization☆40Updated 2 years ago
- Salvaging Federated Learning by Local Adaptation☆55Updated last month
- reveal the vulnerabilities of SplitNN☆30Updated 2 years ago
- ☆27Updated 3 years ago
- ☆14Updated 6 months ago
- Robust aggregation for federated learning with the RFA algorithm.☆42Updated 2 years ago
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆37Updated 2 years ago
- Federated Learning Framework Benchmark (UniFed)☆47Updated last year
- Official code repository for our accepted work "Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning" in NeurI…☆21Updated 11 months ago
- ☆50Updated last year
- PyTorch for benchmarking communication-efficient distributed SGD optimization algorithms☆71Updated 3 years ago
- Learning from history for Byzantine Robustness☆21Updated 3 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Official implementation of our work "Collaborative Fairness in Federated Learning."☆48Updated 3 months ago
- [ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time☆40Updated last year
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆29Updated 2 years ago
- FedDANE: A Federated Newton-Type Method (Asilomar Conference on Signals, Systems, and Computers ‘19)☆24Updated last year
- ☆12Updated last year
- ☆31Updated 4 years ago
- ☆21Updated last year
- ☆38Updated 3 years ago
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆33Updated last year
- Privacy attacks on Split Learning☆37Updated 2 years ago
- A list of papers using/about Federated Learning especially malicious client and attacks.☆12Updated 4 years ago