federated-learning-fairness machine learning
Fairness in Federated Learning Introduction & Motivation Fairness in Federated Learning addresses a central problem in federated and demographic-parity-federated architecture: how…
1,257 technical terms and definitions
Fairness in Federated Learning Introduction & Motivation Fairness in Federated Learning addresses a central problem in federated and demographic-parity-federated architecture: how…
federated learning manufacturing, federated learning, learning manufacturing, 2c 2i
Federated Learning for Manufacturing Introduction & Motivation Federated Learning for Manufacturing addresses a central problem in privacy-aware collaborative modeling: how…
hierarchical federated learning architecture, multi-tier federated
Hierarchical Federated Learning is a multi-tier federated learning architecture that introduces intermediate aggregation layers — instead of all clients communicating directly…
Non-IID Data in Federated Learning Introduction & Motivation Non-IID Data in Federated Learning addresses a central problem in federated and data-heterogeneity-challenges…
ai safety
Federated Learning Poisoning is the exploitation of federated learning's distributed nature to inject malicious model updates — a compromised participant sends poisoned gradient…
distributed training privacy, federated averaging, differential privacy ml, on device training
Federated Learning (FL) is the distributed machine learning paradigm where models are trained across multiple decentralized devices or institutions without centralizing the raw…
federated learning, learning privacy, dp iid ml passed, federated learning & privacy
Federated Learning & Privacy Introduction & Motivation Federated Learning: train models across decentralized data without sharing raw data. Privacy-preserving ML. Applications…
distributed training federated, fedavg federated, privacy preserving ml, federated aggregation
Federated Learning is the distributed machine learning paradigm where multiple clients (devices or organizations) collaboratively train a shared model without exchanging their raw…
distributed model training privacy, differential privacy machine learning, secure aggregation model, federated averaging algorithm
Federated Learning is the distributed machine learning paradigm where multiple clients (mobile devices, hospitals, organizations) collaboratively train a shared model without…
federated averaging algorithm, federated learning communication, non iid data federated, differential privacy federated
Federated Learning is the distributed machine learning paradigm where multiple clients (devices or organizations) collaboratively train a shared model without exchanging raw data…
federated learning privacy and accuracy trade-off prediction, federated learning privacy, learning privacy accuracy, privacy accuracy trade-off, accuracy trade-off prediction
Federated Learning Privacy and Accuracy Trade-Off Prediction with Machine Learning Introduction & Motivation Federated Learning Privacy and Accuracy Trade-Off Prediction…
federated learning privacy and communication, federated learning privacy, learning privacy communication, federated learning, learning privacy, privacy communication
Federated Learning Privacy and Communication Introduction & Motivation Federated Learning Privacy and Communication addresses a central problem in ml-distributed process control…
federated learning privacy, federated learning, learning privacy
Federated Learning Privacy Introduction & Motivation Federated Learning Privacy addresses a central problem in systems process control and communication-efficiency-bandwidth…
Privacy-Preserving Federated Learning Introduction & Motivation Privacy-Preserving Federated Learning addresses a central problem in federated and membership-inference-defense…
Federated Learning Systems Introduction & Motivation Federated Learning Systems addresses a central problem in federated and distributed-system-architecture architecture: how…
FedMA Federated Matched Averaging via Layer Permutation Introduction & Motivation FedMA Federated Matched Averaging via Layer Permutation addresses a central problem in federated…
Federated Matrix Factorization with Item Profile Sharing Introduction & Motivation Federated Matrix Factorization with Item Profile Sharing addresses a central problem…
Federated Meta-Learning Introduction & Motivation Federated Meta-Learning addresses a central problem in federated and rapid-adaptation-federated architecture: how to Implement…
Federated Reptile for Low-Compute Edge Meta-Learning Introduction & Motivation Federated Reptile for Low-Compute Edge Meta-Learning addresses a central problem in federated…
Federated Model Compression Introduction & Motivation Federated Model Compression addresses a central problem in federated and compression-communication-tradeoff architecture: how…
Lottery Ticket Federated Pruning with Shared Subnetworks Introduction & Motivation Lottery Ticket Federated Pruning with Shared Subnetworks addresses a central problem…
Federated Multi-Task Learning Introduction & Motivation Federated Multi-Task Learning addresses a central problem in multitask learning across several related prediction…
Federated Multi-Task Learning (Extended Study) Introduction & Motivation Federated Multi-Task Learning (Extended Study) addresses a central problem in multitask learning across…
Federated Multi-Task Learning (Extended Study) Introduction & Motivation Federated Multi-Task Learning (Extended Study) addresses a central problem in multitask learning across…
FedMultimodal Collaborative Vision-Language Representation Introduction & Motivation FedMultimodal Collaborative Vision-Language Representation addresses a central problem…
Federated Multimodal Learning Introduction & Motivation Federated Multimodal Learning addresses a central problem in multimodal learning and privacy-utility-preservation…
FedNAS Federated Neural Architecture Search via Weight-Sharing Introduction & Motivation FedNAS Federated Neural Architecture Search via Weight-Sharing addresses a central problem…
Federated Neural ODEs for Distributed Continuous Dynamics Introduction & Motivation Federated Neural ODEs for Distributed Continuous Dynamics addresses a central problem…
Federated Isolation Forests for Collaborative Anomaly Detection Introduction & Motivation Federated Isolation Forests for Collaborative Anomaly Detection addresses a central…
federated learning
FedProx (Federated Proximal) is an improvement to FedAvg that adds a proximal term to the local objective — penalizing local models that drift too far from the global model…
Federated Quantum Machine Learning via Parameterized Circuits Introduction & Motivation Federated Quantum Machine Learning via Parameterized Circuits addresses a central problem…
recommendation systems
Federated Rec is federated recommendation training that keeps raw user interaction data on client devices. - It improves privacy by sending model updates instead of centralizing…
Federated Learning for Recommendations Introduction & Motivation Federated Learning for Recommendations addresses a central problem in recsys systems and {target} optimization…
Federated Reinforcement Learning Introduction & Motivation Federated Reinforcement Learning addresses a central problem in federated and policy-aggregation-methods architecture…
Federated Multi-Agent Deep Deterministic Policy Gradients Introduction & Motivation Federated Multi-Agent Deep Deterministic Policy Gradients addresses a central problem…
FedFormer Federated Fine-Tuning of Transformer Layers Introduction & Motivation FedFormer Federated Fine-Tuning of Transformer Layers addresses a central problem in federated…
FedDF Federated Ensemble Distillation with Logit Consensus Introduction & Motivation FedDF Federated Ensemble Distillation with Logit Consensus addresses a central problem…
Federated Semi-Supervised Learning Introduction & Motivation Federated Semi-Supervised Learning addresses a central problem in federated and pseudo-label-federation architecture…
Federated Semi-Supervised Learning with Local Consistency Introduction & Motivation Federated Semi-Supervised Learning with Local Consistency addresses a central problem…
Federated Semi-Supervised Learning Introduction & Motivation Federated Semi-Supervised Learning addresses a central problem in semisupervised and distributed-pseudo-labeling…
Federated Spatiotemporal Kriging for Continental Sensors Introduction & Motivation Federated Spatiotemporal Kriging for Continental Sensors addresses a central problem…
Split Learning with Cut-Layer Activation Streaming Introduction & Motivation Split Learning with Cut-Layer Activation Streaming addresses a central problem in federated…
Online Streaming Federated Learning on Non-Stationary Concepts Introduction & Motivation Online Streaming Federated Learning on Non-Stationary Concepts addresses a central problem…
FedInformer Federated Long-Sequence Time Series Forecasting Introduction & Motivation FedInformer Federated Long-Sequence Time Series Forecasting addresses a central problem…
Top-k Sparsification with Error-Feedback for Federated ML Introduction & Motivation Top-k Sparsification with Error-Feedback for Federated ML addresses a central problem…
FedTopK-Adam with 1-Bit Sign Error Compensation Introduction & Motivation FedTopK-Adam with 1-Bit Sign Error Compensation addresses a central problem in federated…
Federated Transfer Learning FTL for Partial User Alignment Introduction & Motivation Federated Transfer Learning FTL for Partial User Alignment addresses a central problem…
Federated Transfer Learning Introduction & Motivation Federated Transfer Learning addresses a central problem in federated and source-target-alignment-federated architecture: how…
Certified Federated Unlearning via Hessian-Free Residuals Introduction & Motivation Certified Federated Unlearning via Hessian-Free Residuals addresses a central problem…
Fisher-Weighted Certified Federated Data Removal Introduction & Motivation Fisher-Weighted Certified Federated Data Removal addresses a central problem in federated…