https://github.com/mzchen0/UVeQFed
공식 코드UVeQFed: Universal Vector Quantization for Federated Learning
Papers with Code 아카이브
Federated Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 61
Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.
This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mzchen0/Wireless-FL
공식 코드A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
https://github.com/nadavlab/federatedbehrt
공식 코드Federated Learning of Medical Concepts Embedding using BEHRT
https://github.com/naiftt/fedsis
공식 코드https://github.com/naiftt/spafd
공식 코드Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
https://github.com/nanboy-ronan/2f2l-federated-unlearning
공식 코드Forgettable Federated Linear Learning with Certified Data Unlearning
https://github.com/nanboy-ronan/backdoor-fedgan
공식 코드Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis
https://github.com/nchuramani/fcl-mannersdb
공식 코드Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments
https://github.com/ndslab-group/RFL-Self
공식 코드https://github.com/necst/timberstrike
공식 코드https://github.com/negedng/ma-fl-mia
공식 코드https://github.com/neurodata/ProgLearn
공식 코드https://github.com/news-vt/spafl_neruips_2024
공식 코드SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
https://github.com/ngandh/F2LCough
공식 코드Federated Few-shot Learning for Cough Classification with Edge Devices
https://github.com/nguyenhongson1902/venomancer
공식 코드Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning
https://github.com/nhatminh/Dem-AI
공식 코드Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
https://github.com/niko-k98/awesome-list-federated-learning-review
공식 코드Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
https://github.com/nikopavl4/ca_client_selection
공식 코드Intelligent Client Selection for Federated Learning using Cellular Automata
https://github.com/nikosimus/cc-for-br-learning
공식 코드Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
https://github.com/niwanli/semifl_iot
공식 코드Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
https://github.com/nkdinsdale/fedharmony
공식 코드https://github.com/nliulab/FairFML
공식 코드https://github.com/nliulab/FedScore
공식 코드FedScore: A privacy-preserving framework for federated scoring system development
https://github.com/nliulab/fl-benchmark
공식 코드https://github.com/nlokeshiisc/gcfl_release
공식 코드https://github.com/nlokeshiisc/sfedavg-aaai21
공식 코드Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
https://github.com/nokia-bell-labs/data-centric-federated-learning
공식 코드Enhancing Efficiency in Multidevice Federated Learning through Data Selection
https://github.com/noobyzy/FedZO
공식 코드Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
https://github.com/ntobis/federated-machine-learning
공식 코드Personalized Federated Deep Learning for Pain Estimation From Face Images
https://github.com/nuaa-smartsensing/async-fl
공식 코드https://github.com/nubagcilab/icassp2025-fdgpolyp
공식 코드Frequency-Based Federated Domain Generalization for Polyp Segmentation
https://github.com/nusioraprivacy/rflpa
공식 코드RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
https://github.com/nvidia/nvflare
공식 코드NVIDIA FLARE: Federated Learning from Simulation to Real-World
https://github.com/nvlabs/perada
공식 코드PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
https://github.com/officialarijit/dw-fedavg
공식 코드A Dynamic Weighted Federated Learning for Android Malware Classification
https://github.com/ofontenla/fedheonn
공식 코드An effective and efficient green federated learning method for one-layer neural networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.