A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective

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J. Ahn, O. Simeone, and J. Kang. 2019. Wireless federated distillation for distributed edge learning with heterogeneous data. In Proceedings of IEEE PIMRC. 1--6.

Mohammad Mohammadi Amiri and Deniz Gunduz. 2019. Federated learning over wireless fading channels. Retrieved from https://arXiv:1907.09769.

Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, and H. Vincent Poor. 2020. Update Aware Device Scheduling for Federated Learning at the Wireless Edge. Retrieved from https://arxiv:cs.IT/2001.10402.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, and Antonio Ferrara. 2019. Towards Effective Device-aware Federated Learning. Springer International Publishing, Cham, 477--491.

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Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Aguera y Arcas. 2019. Generative Models for Effective ML on Private, Decentralized Datasets. Retrieved from https://arxiv:cs.LG/1911.06679.

Sana Awan, Fengjun Li, Bo Luo, and Mei Liu. 2019. Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain. In Proceedings of SIGSAC’19. ACM, London, UK, 2561--2563.

U. M. Avodji, S. Gambs, and A. Martin. 2019. IOTFLA: A secured and privacy-preserving smart home architecture implementing federated learning. In Proceedings of IEEE SPW. 175--180.

Evita Bakopoulou, Balint Tillman, and Athina Markopoulou. 2019. A federated learning approach for mobile packet classification. Retrieved from https://arXiv:1907.13113.

X. Bao, C. Su, Y. Xiong, W. Huang, and Y. Hu. 2019. FLChain: A blockchain for auditable federated learning with trust and incentive. In Proceedings of BIGCOM. 151--159.

Daniel Benditkis, Aviv Keren, Liron Mor-Yosef, Tomer Avidor, Neta Shoham, and Nadav Tal-Israel. 2019. Distributed deep neural network training on edge devices. In Proceedings of SEC’19. ACM, Arlington, VA, 304--306.

Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. Retrieved from https://arXiv:1812.00984.

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan et al. 2019. Towards federated learning at scale: System design. Retrieved from https://arXiv:1902.01046.

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In Proceedings of SIGSAC’17. ACM, Dallas, TX, 1175--1191.

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2016. Practical secure aggregation for federated learning on user-held data. Retrieved from https://arXiv:1611.04482.

Keith Bonawitz, Fariborz Salehi, Jakub Konečnỳ, Brendan McMahan, and Marco Gruteser. 2019. Federated learning with autotuned communication-efficient secure aggregation. Retrieved from https://arXiv:1912.00131.

Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Kone, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. LEAF: A Benchmark for Federated Settings. Retrieved from https://arxiv:cs.LG/1812.01097.

Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. Retrieved from https://arXiv:1812.07210.

Sebastian Caldas, Virginia Smith, and Ameet Talwalkar. 2018. Federated kernelized multi-task learning. In Proceedings of SysML’18.

Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2019. Secure Federated Matrix Factorization. Retrieved from https://arxiv:cs.CR/1906.05108.

Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. Retrieved from https://arxiv:cs.LG/1802.07876.

M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin. 2020. Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks. IEEE Trans. Wireless Commun. 19, 1 (Jan. 2020), 177--191.

Mingzhe Chen, Zhaohui Yang, Walid Saad, Changchuan Yin, H. Vincent Poor, and Shuguang Cui. 2019. A joint learning and communications framework for federated learning over wireless networks. Retrieved from https://arXiv:1909.07972.

Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, and Mingyi Hong. 2019. Distributed Training with Heterogeneous Data: Bridging Median- and Mean-based Algorithms. Retrieved from https://arxiv:cs.LG/1906.01736.

Yujing Chen, Yue Ning, and Huzefa Rangwala. 2019. Asynchronous online federated learning for edge devices. Retrieved from https://arXiv:1911.02134.

Yudong Chen, Lili Su, and Jiaming Xu. 2018. Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. SIGMETRICS Perform. Eval. Rev. 46, 1 (2018), 96.

Yang Chen, Xiaoyan Sun, and Yaochu Jin. 2019. Communication-efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation. Retrieved from https://arxiv:cs.LG/1903.07424.

Y. Chen, X. Sun, and Y. Jin. 2019. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Trans. Neural Netw. Learn. Syst. (2019), 1--10.

Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang. 2019. SecureBoost: A lossless federated learning framework. Retrieved from https://arXiv:1901.08755.

J. Choi and S. R. Pokhrel. 2019. Federated learning with multichannel ALOHA. IEEE Wireless Commun. Lett. (2019).

Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das. 2019. Differential Privacy-enabled Federated Learning for Sensitive Health Data. Retrieved from https://arxiv:cs.LG/1910.02578.

Luca Corinzia and Joachim M. Buhmann. 2019. Variational federated multi-task learning. Retrieved from https://arXiv:1906.06268.

Harshit Daga, Patrick K. Nicholson, Ada Gavrilovska, and Diego Lugones. 2019. Cartel: A system for collaborative transfer learning at the edge. In Proceedings of SoCC’19. ACM, 25--37.

K. Deng, Z. Chen, S. Zhang, C. Gong, and J. Zhu. 2019. Content compression coding for federated learning. In Proceedings of WCSP’19. 1--6.

Canh Dinh, Nguyen H Tran, Minh NH Nguyen, Choong Seon Hong, Wei Bao, Albert Zomaya, and Vincent Gramoli. 2019. Federated learning over wireless networks: Convergence analysis and resource allocation. Retrieved from https://arXiv:1910.13067.

R. Doku, D. B. Rawat, and C. Liu. 2019. Towards federated learning approach to determine data relevance in big data. In Proceedings of IEEE IRI. 184--192.

Wei Du, Xiao Zeng, Ming Yan, and Mi Zhang. 2018. Efficient federated learning via variational dropout. Openreview.net, 1--12. Retrieved from https://openreview.net/forum?id=BkeAf2CqY7.

Moming Duan. 2019. Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications. Retrieved from https://arXiv:1907.01132.

S. Duan, D. Zhang, Y. Wang, L. Li, and Y. Zhang. 2019. JointRec: A deep learning-based joint cloud video recommendation framework for mobile IoTs. IEEE IoT-J. 7, 3 (2019), 1655--1666.

Amin Fadaeddini, Babak Majidi, and Mohammad Eshghi. 2019. Privacy Preserved Decentralized Deep Learning: A Blockchain-based Solution for Secure AI-driven Enterprise. Springer International Publishing, Cham, 32--40.

Zipei Fan, Xuan Song, Renhe Jiang, Quanjun Chen, and Ryosuke Shibasaki. 2019. Decentralized attention-based personalized human mobility prediction. Proc. IMWUT 3, 4 (2019), Article 133.

S. Feng, D. Niyato, P. Wang, D. I. Kim, and Y. Liang. 2019. Joint service pricing and cooperative relay communication for federated learning. In Proceedings of IEEE GreenCom. 815--820.

Clement Fung, Jamie Koerner, Stewart Grant, and Ivan Beschastnikh. 2018. Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. Retrieved from https://arxiv:cs.CR/1811.09712.

Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. 2018. Mitigating sybils in federated learning poisoning. Retrieved from https://arXiv:1808.04866.

Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. Retrieved from https://arXiv:1712.07557.

Badih Ghazi, Rasmus Pagh, and Ameya Velingker. 2019. Scalable and Differentially Private Distributed Aggregation in the Shuffled Model. Retrieved from https://arxiv:cs.LG/1906.08320.

Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran. 2019. Robust federated learning in a heterogeneous environment. Retrieved from https://arXiv:1906.06629.

Neel Guha, Ameet Talwlkar, and Virginia Smith. 2019. One-shot federated learning. Retrieved from https://arXiv:1902.11175.

Pengchao Han, Shiqiang Wang, and Kin K. Leung. 2020. Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach. Retrieved from https://arxiv:cs.LG/2001.04756.

Yufei Han and Xiangliang Zhang. 2019. Robust federated training via collaborative machine teaching using trusted instances. Retrieved from https://arXiv:1905.02941.

M. Hao, H. Li, X. Luo, G. Xu, H. Yang, and S. Liu. 2020. Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans. Industr. Inform. 16, 10 (2020), 6532--6542.

M. Hao, H. Li, G. Xu, S. Liu, and H. Yang. 2019. Towards efficient and privacy-preserving federated deep learning. In Proceedings of ICC’19. 1--6.

Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018. Federated learning for mobile keyboard prediction. Retrieved from https://arXiv:1811.03604.

Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. Retrieved from https://arXiv:1711.10677.

Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, and Ji Liu. 2019. Central server free federated learning over single-sided trust social networks. Retrieved from https://arXiv:1910.04956.

X. He, Q. Ling, and T. Chen. 2019. Byzantine-robust stochastic gradient descent for distributed low-rank matrix completion. Proceedings of IEEE DSW. 322--326.

Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification. Retrieved from https://arxiv:cs.LG/1909.06335.

B. Hu, Y. Gao, L. Liu, and H. Ma. 2018. Federated region-learning: An edge computing based framework for urban environment sensing. In Proceedings of IEEE GLOBECOM. 1--7.

Chenghao Hu, Jingyan Jiang, and Zhi Wang. 2019. Decentralized federated learning: A segmented gossip approach. Retrieved from https://arXiv:1908.07782.

Yao Hu, Xiaoyan Sun, Yang Chen, and Zishuai Lu. 2019. Model and Feature Aggregation-based Federated Learning for Multi-sensor Time Series Trend Following. Springer International Publishing, Cham, 233--246.

S. Hua, K. Yang, and Y. Shi. 2019. On-device federated learning via second-order optimization with over-the-air computation. In Proceedings of IEEE VTC’19. 1--5.

Li Huang, Andrew L. Shea, Huining Qian, Aditya Masurkar, Hao Deng, and Dianbo Liu. 2019. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed. Inform. 99 (2019), 103291.

Amir Jalalirad, Marco Scavuzzo, Catalin Capota, and Michael Sprague. 2019. A simple and efficient federated recommender system. In Proceedings of BDCAT’19. ACM, Auckland, New Zealand, 53--58.

Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2018. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. Retrieved from https://arXiv:1811.11479.

Shaoxiong Ji, Guodong Long, Shirui Pan, Tianqing Zhu, Jing Jiang, and Sen Wang. 2019. Detecting Suicidal Ideation with Data Protection in Online Communities. Springer International Publishing, Cham, 225--229.

Di Jiang, Yuanfeng Song, Yongxin Tong, Xueyang Wu, Weiwei Zhao, Qian Xu, and Qiang Yang. 2019. Federated topic modeling. In Proceedings of CIKM’19. ACM, 1071--1080.

Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan. 2019. Improving federated learning personalization via model agnostic meta learning. Retrieved from https://arXiv:1909.12488.

Yuang Jiang, Shiqiang Wang, Bong Jun Ko, Wei-Han Lee, and Leandros Tassiulas. 2019. Model pruning enables efficient federated learning on edge devices. Retrieved from https://arXiv:1909.12326.

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurlien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adri Gascon, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Ozgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. 2019. Advances and Open Problems in Federated Learning. Retrieved from https://arxiv:cs.LG/1912.04977.

J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang. 2019. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE IoT-J. 6, 6 (Dec. 2019), 10700--10714.

J. Kang, Z. Xiong, D. Niyato, H. Yu, Y. Liang, and D. I. Kim. 2019. Incentive design for efficient federated learning in mobile networks: A contract theory approach. In Proceedings of IEEE APWCS. 1--5.

J. Kang, Z. Xiong, D. Niyato, Y. Zou, Y. Zhang, and M. Guizani. 2020. Reliable federated learning for mobile networks. IEEE Wirel. Commun. 27, 2 (2020), 72--80.

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. 2019. SCAFFOLD: Stochastic controlled averaging for on-device federated learning. Retrieved from https://arXiv:1910.06378.

Latif U. Khan, Nguyen H. Tran, Shashi Raj Pandey, Walid Saad, Zhu Han, Minh N. H. Nguyen, and Choong Seon Hong. 2019. Federated learning for edge networks: Resource optimization and incentive mechanism. Retrieved from https://arXiv:1911.05642.

H. Kim, J. Park, M. Bennis, and S. Kim. 2019. Blockchained on-device federated learning. IEEE Commun. Lett. 24, 6 (2019), 1279--1283.

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Jakub Kone, H. Brendan McMahan, Daniel Ramage, and Peter Richtrik. 2016. Federated Optimization: Distributed Machine Learning for On-Device Intelligence. Retrieved from https://arxiv:cs.LG/1610.02527.

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Hongyu Li and Tianqi Han. 2019. An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning. Retrieved from https://arxiv:cs.LG/1908.08340.

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