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The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge.
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An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance
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Accurate, large minibatch sgd: Training imagenet in 1 hour
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The eu general data protection regulation (gdpr)
P. Voigt and A. Von dem Bussche · 2017
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A cloud-edge computing framework for cyber-physical-social services
X. Wang, L. T. Yang, X. Xie, J. Jin, and M. J. Deen · 2017
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A survey on the edge computing for the internet of things
W. Yu, F. Liang, X. He, W. G. Hatcher, C. Lu, J. Lin, and X. Yang · 2017
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Task offloading for mobile edge computing in software defined ultra-dense network
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Distributed learning of deep neural network over multiple agents
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Federated learning for mobile keyboard prediction
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Fully decentralized federated learning
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On the convergence of federated optimization in heterogeneous networks
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Joint task offloading and resource allocation for multi-server mobile-edge computing networks
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Split learning for health: Distributed deep learning without sharing raw patient data
P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar · 2018
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Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing
J. Zhang, W. Xia, F. Yan, and L. Shen · 2018
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Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
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Towards federated learning at scale: System design
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Gossip learning as a decentralized alternative to federated learning
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Decentralized federated learning: A segmented gossip approach
C. Hu, J. Jiang, and Z. Wang · 2019
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On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2019
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Blockchain and federated learning for privacy-preserved data sharing in industrial iot
Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang · 2019
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Flchain: Federated learning via mec-enabled blockchain network
U. Majeed and C. S. Hong · 2019
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Communication-efficient federated learning for wireless edge intelligence in iot
J. Mills, J. Hu, and G. Min · 2019
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Braintorrent: A peer-to-peer environment for decentralized federated learning
A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger · 2019
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Joint offloading and computation energy efficiency maximization in a mobile edge computing system
H. Sun, F. Zhou, and R. Q. Hu · 2019
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Asynchronous federated optimization
C. Xie, S. Koyejo, and I. Gupta · 2019
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Safelearn: Secure aggregation for private federated learning
H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, H. Möllering, T. D. Nguyen, P. Rieger, A.-R. Sadeghi, T. Schneider, H. Yalame, et al · 2021
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Spreadgnn: Serverless multi-task federated learning for graph neural networks
C. He, E. Ceyani, K. Balasubramanian, M. Annavaram, and S. Avestimehr · 2021
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Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
S. Horvath, S. Laskaridis, M. Almeida, I. Leontiadis, S. Venieris, and N. Lane · 2021
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Joint topology and computation resource optimization for federated edge learning
S. Huang, S. Wang, R. Wang, and K. Huang · 2021
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2021
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A computation offloading method over big data for iot-enabled cloud-edge computing
X. Xu, Q. Liu, Y. Luo, K. Peng, X. Zhang, S. Meng, and L. Qi · 2019
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Federated machine learning: Concept and applications
Q. Yang, Y. Liu, T. Chen, and Y. Tong · 2019
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Secure single-server aggregation with (poly) logarithmic overhead
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova · 2020
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Federated learning with hierarchical clustering of local updates to improve training on non-iid data
C. Briggs, Z. Fan, and P. Andras · 2020
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Communication-computation efficient secure aggregation for federated learning
B. Choi, J.-y. Sohn, D.-J. Han, and J. Moon · 2020
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A survey of hierarchical energy optimization for mobile edge computing: A perspective from end devices to the cloud
P. Cong, J. Zhou, L. Li, K. Cao, T. Wei, and K. Li · 2020
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Self-balancing federated learning with global imbalanced data in mobile systems
M. Duan, D. Liu, X. Chen, R. Liu, Y. Tan, and L. Liang · 2020
Cited alongside, same era.
Impact of network topology on the convergence of decentralized federated learning systems
H. Kavalionak, E. Carlini, P. Dazzi, L. Ferrucci, M. Mordacchini, and M. Coppola · 2021
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Federated learning for internet of things: Recent advances, taxonomy, and open challenges
L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong · 2021
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Pefl: Deep privacy-encoding based federated learning framework for smart agriculture
P. Kumar, G. P. Gupta, and R. Tripathi · 2021
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Semi-decentralized federated learning with cooperative d2d local model aggregations
F. P.-C. Lin, S. Hosseinalipour, S. S. Azam, C. G. Brinton, and N. Michelusi · 2021
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Decentralized federated learning: Balancing communication and computing costs
W. Liu, L. Chen, and W. Zhang · 2021
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Fedsa: A semi-asynchronous federated learning mechanism in heterogeneous edge computing
Q. Ma, Y. Xu, H. Xu, Z. Jiang, L. Huang, and H. Huang · 2021
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Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints
N. Mhaisen, A. Awad, A. Mohamed, A. Erbad, and M. Guizani · 2021
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A survey on security and privacy of federated learning
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava · 2021
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Federated learning in a medical context: a systematic literature review
B. Pfitzner, N. Steckhan, and B. Arnrich · 2021
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Permissioned blockchain frameworks in the industry: A comparison
J. Polge, J. Robert, and Y. Le Traon · 2021
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Towards flexible device participation in federated learning
Y. Ruan, X. Zhang, S.-C. Liang, and C. Joe-Wong · 2021
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Over-the-air decentralized federated learning
Y. Shi, Y. Zhou, and Y. Shi · 2021
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A survey on mobile augmented reality with 5g mobile edge computing: architectures, applications, and technical aspects
Y. Siriwardhana, P. Porambage, M. Liyanage, and M. Ylianttila · 2021
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Semi-synchronous federated learning
D. Stripelis and J. L. Ambite · 2021
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Efficient parameter aggregation in federated learning with hybrid convergecast
Y. Tao, J. Zhou, and S. Yu · 2021
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Ringfed: Reducing communication costs in federated learning on non-iid data
G. Yang, K. Mu, C. Song, Z. Yang, and T. Gong · 2021
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Toward resource-efficient federated learning in mobile edge computing
R. Yu and P. Li · 2021
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A survey on federated learning
C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao · 2021
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P-fedavg: parallelizing federated learning with theoretical guarantees
Z. Zhong, Y. Zhou, D. Wu, X. Chen, M. Chen, C. Li, and Q. Z. Sheng · 2021
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Two-layer federated learning with heterogeneous model aggregation for 6g supported internet of vehicles
X. Zhou, W. Liang, J. She, Z. Yan, I. Kevin, and K. Wang · 2021
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Federated learning challenges and opportunities: An outlook
J. Ding, E. Tramel, A. K. Sahu, S. Wu, S. Avestimehr, and T. Zhang · 2022
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Basil: A fast and byzantine-resilient approach for decentralized training
A. R. Elkordy, S. Prakash, and S. Avestimehr · 2022
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Trusted decentralized federated learning
A. Gholami, N. Torkzaban, and J. S. Baras · 2022
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Distributed machine learning for multiuser mobile edge computing systems
Y. Guo, R. Zhao, S. Lai, L. Fan, X. Lei, and G. K. Karagiannidis · 2022
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Improving accuracy and convergence in group-based federated learning on non-iid data
Z. He, L. Yang, W. Lin, and W. Wu · 2022
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A review on federated learning towards image processing
F. A. KhoKhar, J. H. Shah, M. A. Khan, M. Sharif, U. Tariq, and S. Kadry · 2022
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Z. Li, J. Lu, S. Luo, D. Zhu, Y. Shao, Y. Li, Z. Zhang, and C. Wu · 2022
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Star-ris integrated nonorthogonal multiple access and over-the-air federated learning: Framework, analysis, and optimization
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Federated learning with partial model personalization
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Federated learning for distributed spectrum sensing in nextg communication networks
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Towards personalized federated learning
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Gait identification using limb joint movement and deep machine learning
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Accelerating federated learning with cluster construction and hierarchical aggregation
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Vertical federated learning: Challenges, methodologies and experiments
K. Wei, J. Li, C. Ma, M. Ding, S. Wei, F. Wu, G. Chen, and T. Ranbaduge · 2022
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Joint scheduling and resource allocation for hierarchical federated edge learning
W. Wen, Z. Chen, H. H. Yang, W. Xia, and T. Q. Quek · 2022
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M. Yemini, R. Saha, E. Ozfatura, D. Gündüz, and A. J. Goldsmith · 2022
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Decentralized event-triggered federated learning with heterogeneous communication thresholds
S. Zehtabi, S. Hosseinalipour, and C. G. Brinton · 2022
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Efficient personalized federated learning via sparse model-adaptation
D. Chen, L. Yao, D. Gao, B. Ding, and Y. Li · 2023
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Federated learning over images: Vertical decompositions and pre-trained backbones are difficult to beat
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Outdoor mobility aid for people with visual impairment: Obstacle detection and responsive framework for the scene perception during the outdoor mobility of people with visual impairment
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