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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Elastic Deep Learning Using Knowledge Distillation with Heterogeneous Computing Resources
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HRank: Filter Pruning Using High-Rank Feature Map
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Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
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FedAux: An Efficient Framework for Hybrid Federated Learning
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Federated Fingerprint Learning with Heterogeneous Architectures
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Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning
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Jensen-Shannon divergence and Hilbert space embedding
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Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
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On the Convergence of Hybrid Server-Clients Collaborative Training
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Usability Enhanced Thumbnail-Preserving Encryption Based on Data Hiding for JPEG Images
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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
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Toward trustworthy crowdsourcing in the social internet of things
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Accelerating Federated Learning With a Global Biased Optimiser
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Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System
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An Efficient Public Auditing Protocol With Novel Dynamic Structure for Cloud Data
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An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System
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Model Pruning Enables Efficient Federated Learning on Edge Devices
inferredcites →https://doi.org/10.1109/tpds.2020.2975189openalex-snapshot
Accelerating Federated Learning via Momentum Gradient Descent
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Multi-Job Intelligent Scheduling With Cross-Device Federated Learning
inferredcites →https://doi.org/10.1137/1.9781611978032.95openalex-snapshot
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
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FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
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Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
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Advances and Open Problems in Federated Learning
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Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
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On the Convergence of Communication-Efficient Local SGD for Federated Learning
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Efficient Device Scheduling with Multi-Job Federated Learning
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FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.488openalex-snapshot
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
inferredcites →https://doi.org/10.24963/ijcai.2018/447openalex-snapshot
On the Convergence Properties of a K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization
inferredcites →https://doi.org/10.24963/ijcai.2021/205openalex-snapshot
Practical One-Shot Federated Learning for Cross-Silo Setting
inferredcites →https://doi.org/10.24963/ijcai.2022/385openalex-snapshot
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
inferredcites →https://doi.org/10.24963/ijcai.2023/394openalex-snapshot
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1806.00582openalex-snapshot
Federated Learning with Non-IID Data
inferredcites →https://doi.org/10.48550/arxiv.1811.11479openalex-snapshot
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
inferredcites →https://doi.org/10.48550/arxiv.2008.03606openalex-snapshot
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
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FedMix: Approximation of Mixup under Mean Augmented Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2310.15080openalex-snapshot
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
inferredcites →https://doi.org/10.5040/9781509967339.v2_0031openalex-snapshot
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Genetic Algorithm-Based Parameter Selection for Communication-Efficient Federated Learning
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Trustworthy federated learning: privacy, security, and beyond
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Multi-interest transfer using contrastive learning for cross-domain recommendation
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inferred← citeshttps://doi.org/10.1145/3716870openalex-snapshot
Communication-Efficient Federated Learning for Heterogeneous Clients
inferred← citeshttps://doi.org/10.1145/3749376openalex-snapshot
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
inferred← citeshttps://doi.org/10.1145/3832321openalex-snapshot
SHAM-AI: A Self-Healing and Adaptive Multi-Modal Anomaly Intelligence Framework for Securing Cloud Digital Twins
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Optimized tourist point-of-interest recommendation through ARIMA and SVD in edge environment
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DIGNN-A: Real-Time Network Intrusion Detection with Integrated Neural Networks Based on Dynamic Graph
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