https://github.com/okkomakkonen/label-heterogeneity
공식 코드Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
Papers with Code 아카이브
Federated Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/omarfoq/communication-in-cross-silo-fl
공식 코드Throughput-Optimal Topology Design for Cross-Silo Federated Learning
https://github.com/omarfoq/fedem
공식 코드Federated Multi-Task Learning under a Mixture of Distributions
https://github.com/omarfoq/knn-per
공식 코드https://github.com/omarfoq/streaming-fl
공식 코드https://github.com/omrilev1/gaussmix
공식 코드The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
https://github.com/ongwinkent/federated-feature-unlearning
공식 코드Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
https://github.com/onizukalab/fedbench
공식 코드https://github.com/open-digital-twin/framework-fl-testing
공식 코드A Framework for testing Federated Learning algorithms using an edge-like environment
https://github.com/optimization-ai/icml2021_feddeepauc_codasca
공식 코드Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
https://github.com/optimization-ai/icml2023_fedxl
공식 코드FeDXL: Provable Federated Learning for Deep X-Risk Optimization
https://github.com/orienfish/async-hfl
공식 코드Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
https://github.com/orion-orion/FedDCSR
공식 코드FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
https://github.com/orion-orion/fedhcdr
공식 코드FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
https://github.com/orionw/multilingual-federated-learning
공식 코드https://github.com/oritalp/PAUSE
공식 코드PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
https://github.com/oscardilley/federated-fairness
공식 코드Federated Fairness Analytics: Quantifying Fairness in Federated Learning
https://github.com/osmhpi/federated-learning-dag
공식 코드Implicit Model Specialization through DAG-based Decentralized Federated Learning
https://github.com/osu-nlp-group/fl4semanticparsing
공식 코드Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
https://github.com/osu-srml/upcycled-fl
공식 코드Federated Learning with Reduced Information Leakage and Computation
https://github.com/owkin/fedeca
공식 코드https://github.com/owkin/flamby
공식 코드FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
https://github.com/owkin/idash-2020
공식 코드Differentially Private Federated Learning for Cancer Prediction
https://github.com/owkin/sratta
공식 코드SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
https://github.com/oyhah/BROADCAST
공식 코드https://github.com/oyhah/dp-rsa
공식 코드Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation
https://github.com/p3eft-iclr-2025/p3eft-iclr-2025
공식 코드Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
https://github.com/padillma1/heart-disease-classification-on-uci-dataset-and-shapley-interpretability-analysis
공식 코드https://github.com/pandrei7/paper-synthesizing-histopathology
공식 코드Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images
https://github.com/papersubmission678/the-cost-of-local-and-global-fairness-in-fl
공식 코드https://github.com/paritybit-ai/xfl
공식 코드XFL: A High Performace, Lightweighted Federated Learning Framework
https://github.com/pasquini-dario/SplitNN_FSHA
공식 코드https://github.com/pasquini-dario/eludingsecureaggregation
공식 코드Eluding Secure Aggregation in Federated Learning via Model Inconsistency
https://github.com/patrickitwm/dwf-k-means
공식 코드https://github.com/pechen123/2dbn_learning
공식 코드Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data
https://github.com/pengj97/byzantine-robust-variance-reduction
공식 코드Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.