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Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Federated Transfer Reinforcement Learning for Autonomous Driving
inferredcites →https://doi.org/10.1007/978-3-031-11748-0_2openalex-snapshot
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art
inferredcites →https://doi.org/10.1016/j.artint.2025.104333openalex-snapshot
FedHM: Efficient federated learning for heterogeneous models via low-rank factorization
inferredcites →https://doi.org/10.1016/j.cosrev.2023.100595openalex-snapshot
Asynchronous federated learning on heterogeneous devices: A survey
inferredcites →https://doi.org/10.1016/j.jpowsour.2006.10.046openalex-snapshot
Capacity and power fading mechanism identification from a commercial cell evaluation
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
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An Open Framework for Participatory PM2.5 Monitoring in Smart Cities
inferredcites →https://doi.org/10.1109/comst.2020.3007787openalex-snapshot
Communication-Efficient Edge AI: Algorithms and Systems
inferredcites →https://doi.org/10.1109/comst.2021.3090430openalex-snapshot
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
inferredcites →https://doi.org/10.1109/icc.1995openalex-snapshot
Proceedings IEEE International Conference on Communications ICC '95
inferredcites →https://doi.org/10.1109/jiot.2017.2766085openalex-snapshot
ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems
inferredcites →https://doi.org/10.1109/jiot.2019.2893866openalex-snapshot
From Cloud Down to Things: An Overview of Machine Learning in Internet of Things
inferredcites →https://doi.org/10.1109/jiot.2019.2956615openalex-snapshot
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
inferredcites →https://doi.org/10.1109/jiot.2020.3030072openalex-snapshot
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
inferredcites →https://doi.org/10.1109/jiot.2021.3095077openalex-snapshot
A Survey on Federated Learning for Resource-Constrained IoT Devices
inferredcites →https://doi.org/10.1109/jsac.2019.2904348openalex-snapshot
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
inferredcites →https://doi.org/10.1109/jsait.2022.3205475openalex-snapshot
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity
inferredcites →https://doi.org/10.1109/lra.2019.2931179openalex-snapshot
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
inferredcites →https://doi.org/10.1109/micro.1996openalex-snapshot
Proceedings of the 29th Annual IEEE/ACM International Symposium on Microarchitecture. MICRO 29
inferredcites →https://doi.org/10.1109/mis.2020.2988604openalex-snapshot
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
inferredcites →https://doi.org/10.1109/mnet.011.2000295openalex-snapshot
Toward Resource-Efficient Federated Learning in Mobile Edge Computing
inferredcites →https://doi.org/10.1109/mnet.011.2000430openalex-snapshot
Federated Learning in Vehicular Networks: Opportunities and Solutions
inferredcites →https://doi.org/10.1109/msn53354.2021.00043openalex-snapshot
FedHe: Heterogeneous Models and Communication-Efficient Federated Learning
inferredcites →https://doi.org/10.1109/msp.2012.2211477openalex-snapshot
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
inferredcites →https://doi.org/10.1109/msp.2020.2975749openalex-snapshot
Federated Learning: Challenges, Methods, and Future Directions
inferredcites →https://doi.org/10.1109/tcsi.2017.2717790openalex-snapshot
Reliability in Super- and Near-Threshold Computing: A Unified Model of RTN, BTI, and PV
inferredcites →https://doi.org/10.1109/tifs.2020.2988575openalex-snapshot
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
inferredcites →https://doi.org/10.1109/tnnls.2022.3166101openalex-snapshot
Model Pruning Enables Efficient Federated Learning on Edge Devices
inferredcites →https://doi.org/10.1109/tpds.2020.3023905openalex-snapshot
Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity
inferredcites →https://doi.org/10.1109/twc.2020.2974748openalex-snapshot
Federated Learning Over Wireless Fading Channels
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
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Proceedings of the 25th international conference on Machine learning - ICML '08
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TiFL: A Tier-based Federated Learning System
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Decentralised Learning in Federated Deployment Environments
inferredcites →https://doi.org/10.1145/3450288openalex-snapshot
A Systematic Literature Review on Federated Machine Learning
inferredcites →https://doi.org/10.1145/3450494openalex-snapshot
A Survey of On-Device Machine Learning
inferredcites →https://doi.org/10.1145/3495243openalex-snapshot
Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
inferredcites →https://doi.org/10.1145/3523227.3546759openalex-snapshot
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
inferredcites →https://doi.org/10.1145/3527156openalex-snapshot
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
inferredcites →https://doi.org/10.23919/jcin.2021.9475121openalex-snapshot
Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1702.05373openalex-snapshot
EMNIST: an extension of MNIST to handwritten letters
inferredcites →https://doi.org/10.48550/arxiv.1702.05800openalex-snapshot
Revisiting Distributed Synchronous SGD
inferredcites →https://doi.org/10.48550/arxiv.1812.02903openalex-snapshot
Applied Federated Learning: Improving Google Keyboard Query Suggestions
inferredcites →https://doi.org/10.48550/arxiv.1903.03934openalex-snapshot
Asynchronous Federated Optimization
inferredcites →https://doi.org/10.48550/arxiv.1909.05784openalex-snapshot
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
inferredcites →https://doi.org/10.48550/arxiv.1912.11279openalex-snapshot
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
inferredcites →https://doi.org/10.48550/arxiv.2005.05083openalex-snapshot
A Federated Learning Framework for Healthcare IoT devices
inferredcites →https://doi.org/10.48550/arxiv.2111.04877openalex-snapshot
Papaya: Practical, Private, and Scalable Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2112.01637openalex-snapshot
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.2203.05468openalex-snapshot
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.70432openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-71464-1_41openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-82240-7_10openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-24926-5_16openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-96-1545-2_13openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00530-024-01386-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11227-026-08314-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s44163-026-01171-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.adhoc.2024.103728openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.comnet.2024.110663openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111468openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2026.131199openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.future.2024.107484openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104393openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.iot.2025.101632openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.iot.2025.101730openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.mlwa.2026.100944openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133666openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108038openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neunet.2026.109261openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41746-024-00998-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1049/2024/8821891openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/aectsd65988.2025.11410725openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/globecom59602.2025.11432132openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/globecom59602.2025.11432772openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icdcsw63273.2025.00014openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tii.2025.3650073openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmc.2025.3647655openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/trustcom63139.2024.00099openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/wcnc65185.2026.11555362openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/exsy.70145openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/exsy.70271openalex-snapshot
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inferred← citeshttps://doi.org/10.1142/s0218126626450040openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3636534.3690705openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3748607openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/acis/4260698openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13677-024-00700-1openalex-snapshot
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inferred← citeshttps://doi.org/10.18653/v1/2023.acl-long.193openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-9263961/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-9548238/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.23919/date64628.2025.10992947openalex-snapshot
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inferred← citeshttps://doi.org/10.23919/date69613.2026.11539679openalex-snapshot
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inferred← citeshttps://doi.org/10.31449/inf.v49i17.7707openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/electronics15132876openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/fi17010018openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/info16010014openalex-snapshot
A Trusted Federated Learning Method Based on Consortium Blockchain
inferred← citeshttps://doi.org/10.3390/jmse14191837openalex-snapshot
Role-Dependent Mission-Energy Reserve Control for Federated Learning in Solar-Powered Maritime Edge Networks
inferred← citeshttps://doi.org/10.3390/smartcities8010035openalex-snapshot
Enhancing Smart Home Security: Blockchain-Enabled Federated Learning with Knowledge Distillation for Intrusion Detection
inferred← citeshttps://doi.org/10.35193/bseufbd.1756289openalex-snapshot
Data Privacy in Machine Learning: Challenges and Federated Learning Solutions
inferred← citeshttps://doi.org/10.48084/etasr.15817openalex-snapshot
FedXChain: Explainable Federated Learning with Adaptive Trust Scoring and Blockchain-Based Audit Trails
inferred← citeshttps://doi.org/10.53894/ijirss.v8i5.9078openalex-snapshot
Intelligent IoT forensics: Secure evidence acquisition and autonomous intrusion detection
inferred← citeshttps://doi.org/10.5753/jisa.2024.4006openalex-snapshot
The Impact of Federated Learning on Urban Computing
inferred← citeshttps://doi.org/10.67378/gfpben57openalex-snapshot
Federated Learning, Attack Detection and Distributed Denial of Service: A Cloud Computing Environment Study
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