Pei et al., 2024 - Google Patents
A review of federated learning methods in heterogeneous scenariosPei et al., 2024
- Document ID
- 2998576242975731409
- Author
- Pei J
- Liu W
- Li J
- Wang L
- Liu C
- Publication year
- Publication venue
- IEEE Transactions on Consumer Electronics
External Links
Snippet
Federated learning emerges as a solution to the dilemma of data silos while safeguarding data privacy, particularly relevant in the consumer electronics sector where user data privacy is paramount. However, federated learning is generally employed in a heterogeneous …
- 238000000034 method 0 title description 74
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pei et al. | A review of federated learning methods in heterogeneous scenarios | |
| Zhou et al. | Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks | |
| Zhang et al. | Efficient federated learning for cloud-based AIoT applications | |
| Xu et al. | Edge intelligence: Architectures, challenges, and applications | |
| Qiao et al. | Transitioning from federated learning to quantum federated learning in internet of things: A comprehensive survey | |
| Li et al. | Feature matching data synthesis for non-iid federated learning | |
| Liao et al. | MergeSFL: Split federated learning with feature merging and batch size regulation | |
| Zhang et al. | FedCR: Personalized federated learning based on across-client common representation with conditional mutual information regularization | |
| Ren et al. | Advances and open challenges in federated foundation models | |
| Balaji et al. | Dynamic distributed generative adversarial network for intrusion detection system over internet of things | |
| Wu et al. | A federated deep learning framework for privacy-preserving consumer electronics recommendations | |
| Uddin et al. | Federated learning via disentangled information bottleneck | |
| Li et al. | An efficient architecture for imputing distributed data sets of IoT networks | |
| Yan et al. | Balancing similarity and complementarity for federated learning | |
| Yang et al. | Lstm network-based adaptation approach for dynamic integration in intelligent end-edge-cloud systems | |
| Fan et al. | A survey on model-heterogeneous federated learning: Problems, methods, and prospects | |
| Cheng et al. | Snapcfl: A pre-clustering-based clustered federated learning framework for data and system heterogeneities | |
| Thakur et al. | Hardware-algorithm co-design of energy efficient federated learning in quantized neural network | |
| Sai et al. | A blockchain-enabled split learning framework with a novel client selection method for collaborative learning in smart healthcare | |
| Sah et al. | Aggregation techniques in federated learning: Comprehensive survey, challenges and opportunities | |
| Jiang et al. | Semi-supervised decentralized machine learning with device-to-device cooperation | |
| Qian et al. | Robustness analytics to data heterogeneity in edge computing | |
| Wu et al. | Edge computing and few-shot learning featured intelligent framework in digital twin empowered mobile networks | |
| Yu et al. | Graph-based joint client clustering and resource allocation for wireless distributed learning: A new hierarchical federated learning framework with non-IID data | |
| Kaur et al. | Federated Learning in IoT: A Survey from a Resource-Constrained Perspective |