[go: up one dir, main page]

Chadha et al., 2020 - Google Patents

Towards federated learning using faas fabric

Chadha et al., 2020

View PDF
Document ID
15316124021174849648
Author
Chadha M
Jindal A
Gerndt M
Publication year
Publication venue
Proceedings of the 2020 sixth international workshop on serverless computing

External Links

Snippet

Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061Partitioning or combining of resources
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5038Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30067File systems; File servers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/06Buying, selling or leasing transactions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F8/00Arrangements for software engineering
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/10Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network

Similar Documents

Publication Publication Date Title
Chadha et al. Towards federated learning using faas fabric
US10963313B2 (en) Automated reinforcement-learning-based application manager that learns and improves a reward function
EP4142427A1 (en) Distributed data nodes for flexible data mesh architectures
Rahman et al. Adaptive workflow scheduling for dynamic grid and cloud computing environment
Nasirifard et al. FabricCRDT: A conflict-free replicated datatypes approach to permissioned blockchains
US20180060106A1 (en) Multi-tiered-application distribution to resource-provider hosts by an automated resource-exchange system
US20150178052A1 (en) Automated experimentation platform
Sujana et al. Smart PSO-based secured scheduling approaches for scientific workflows in cloud computing
US10970649B2 (en) Automated reinforcement-learning-based application manager that uses local agents
US20160110434A1 (en) Method and system that determine whether or not two graph-like representations of two systems describe equivalent systems
Liu et al. MapReduce based parallel neural networks in enabling large scale machine learning
CN106687918A (en) Compiling graph-based program specifications
US11042640B2 (en) Safe-operation-constrained reinforcement-learning-based application manager
US20200065118A1 (en) Administrator-monitored reinforcement-learning-based application manager
Kijsipongse et al. A hybrid GPU cluster and volunteer computing platform for scalable deep learning
US11080623B2 (en) Automated reinforcement-learning-based application manager that uses action tags and metric tags
Sankar et al. Efficient method for parallel process and matching of large data set in grid computing environment
Zhang et al. Learning driven parallelization for large-scale video workload in hybrid CPU-GPU cluster
Zhiyong et al. An improved container cloud resource scheduling strategy
US20240112067A1 (en) Managed solver execution using different solver types
Tiwari et al. A review of particle swarm optimization in cloud computing
Mateos et al. The Cloud at Your Service: The when, how, and why of enterprise cloud computing
Wan et al. An Improved Coral Reef Optimization‐Based Scheduling Algorithm for Cloud Computing
Vostokin et al. Implementation of frequency analysis of twitter microblogging in a hybrid cloud based on the Binder, Everest platform and the Samara University virtual desktop service
US20240111832A1 (en) Solver execution service management