1,215 technical terms and definitions
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…
Zero-Knowledge Proofs for Auditable Federated Training Introduction & Motivation Zero-Knowledge Proofs for Auditable Federated Training addresses a central problem in federated…
time series models
FEDformer is frequency-enhanced decomposition transformer for efficient long-term time-series forecasting. - It performs attention in frequency space to exploit sparse spectral…
federated learning
FedNova (Federated Normalized Averaging) is a federated learning algorithm that normalizes client updates to account for different numbers of local steps — fixing the objective…
federated learning
FedOpt (Federated Optimization) is a framework that applies server-side adaptive optimizers (Adam, Adagrad, Yogi) to aggregate client updates — instead of simple averaging…
FedOpt Adaptive Server Optimizers FedAdam and FedYogi Introduction & Motivation FedOpt Adaptive Server Optimizers FedAdam and FedYogi addresses a central problem in federated…
federated learning
FedPer (Federated Personalization) is a personalized federated learning approach that splits the model into shared base layers and private personalization layers — the base layers…
FedProx Proximal Term Regularization for Non-IID Clients Introduction & Motivation FedProx Proximal Term Regularization for Non-IID Clients addresses a central problem…
process control
Feed-Forward Control is a process control strategy that uses upstream measurements to adjust downstream process parameters — compensating for known incoming variations before they…
thumbs, rating
Feedback Collecting user feedback on AI outputs through thumbs up/down, ratings, corrections, and explicit preferences provides essential signal for improving prompts, fine-tuning…
process control
Feedback Control is a process control strategy that uses downstream measurements (after processing) to adjust the process recipe for subsequent lots — correcting for systematic…
manufacturing operations
Feedback Control is closed-loop adjustment that uses measured post-process error to correct subsequent processing - It is a core method in modern semiconductor wafer-map analytics…