[ 1 ] 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要 [EB/OL]. (2021-03-13)[2023-11-15]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm.
The outline of the Fourteenth Five-year Plan for national economic and social development of the People´s Republic of China and the vision 2035 [EB/OL]. (2021-03-13)‍[2023-11-15]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm.

[ 2 ] 张依琳, 梁玉珠, 尹沐君, 等‍. 移动边缘计算中计算卸载方案研究综述 [J]. 计算机学报, 2021, 44(12): 2406‒2430.
Zhang Y L, Liang Y Z, Yin M J, et al. Survey on the methods of computation offloading in mobile edge computing [J]. Chinese Journal of Computers, 2021, 44(12): 2406‒2430.

[ 3 ] 贾晓千, 陈刚, 李白冰‍. 边缘计算在视频侦查中的应用 [J]. 计算机工程与应用, 2020, 56(17): 86‒92.
Jia X Q, Chen G, Li B B. Application of edge computing in video investigation [J]. Computer Engineering and Applications, 2020, 56(17): 86‒92.

[ 4 ] 曹行健, 张志涛, 孙彦赞, 等‍. 面向智慧交通的图像处理与边缘计算 [J]. 中国图象图形学报, 2022, 27(6): 1743‒1767.
Cao X J, Zhang Z T, Sun Y Z, et al. The review of image processing and edge computing for intelligent transportation system [J]. Journal of Image and Graphics, 2022, 27(6): 1743‒1767.

[ 5 ] 王智, 夏树涛, 毛睿‍. 基于边缘智能的沉浸式元宇宙关键技术与展望 [J/OL]. 大数据, [2023-11-13]. https://link.cnki.net/urlid/10.1321.G2.20231110.1017.004.
Wang Z, Xia S T, Mao R. Edge-intelligence-based immersive metaverse: Key technologies and prospects [J/OL]. Big Data Research, [2023-11-13]. https://link.cnki.net/urlid/10.1321.G2.20231110.1017.004.

[ 6 ] 高祥云, 孟丹, 罗明凯, 等. 支持隐私保护的端云协同训练 [J]. 华东师范大学学报 (自然科学版), 2023 (5): 77‒89.
Gao X Y, Meng D, Luo M K, et al. Privacy-preserving cloud-end collaborative training [J]. Journal of East China Normal University (Natural Science), 2023 (5): 77‒89.

[ 7 ] 杨洋, 况琨, 陈政聿, 等. 基于端云协同体系的预训练大模型及其服务化 [J]. 人工智能, 2022, 9(6): 103‒120.
Yang Y, Kuang K, Chen Z Y, et al. Pre-training model based on end-cloud collaboration system and its service [J]. AI-View, 2022, 9(6): 103‒120.

[ 8 ] Lecun Y, Denker J S, Solla S A, et al. Optimal brain damage [C]. Denver: Advances in Neural Information Processing Systems 2, 1989.

[ 9 ] Han S, Mao H Z, Dally W J. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding [EB/OL]. (2015-10-01)[2023-11-15]. https://arxiv.org/abs/1510.00149.

[10] McMahan H B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data [EB/OL]. (2016-02-17)[2023-11-15]. https://arxiv.org/abs/1602.05629.

[11] Li Y G, Yu R, Shahabi C, et al. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting [EB/OL]. (2017-07-06)[2023-11-15]. https://arxiv.org/abs/1707.01926.

[12] Li T, Sahu A K, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions [J]. IEEE Signal Processing Magazine, 2020, 37(3): 50‒60.

[13] Yao J C, Wang F, Jia K Y, et al. Device-cloud collaborative learning for recommendation [EB/OL]. (2021-04-14)[2023-11-15]. https://arxiv.org/abs/2104.06624.

[14] Sim K C, Zadrazil P, Beaufays F. An investigation into on-device personalization of end-to-end automatic speech recognition models [EB/OL]. (2019-09-14)[2023-11-15]. https://arxiv.org/abs/1909. 06678.

[15] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks [EB/OL]. (2017-03-09)[2023-11-15]. https://arxiv.org/abs/1703.03400.

[16] Tan A Z, Yu H, Cui L Z, et al. Towards personalized federated learning [J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(12): 9587‒9603.

[17] Deng S G, Zhao H L, Fang W J, et al. Edge intelligence: The confluence of edge computing and artificial intelligence [J]. IEEE Internet of Things Journal, 2020, 7(8): 7457‒7469.

[18] Pimenidis E, Polatidis N, Mouratidis H. Mobile recommender systems: Identifying the major concepts [J]. Journal of Information Science, 2019, 45(3): 387‒397.

[19] Shuja J, Bilal K, Alasmary W, et al. Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey [J]. Journal of Network and Computer Applications, 2021, 181: 103005.

[20] Cisco global cloud index: Forecast and methodology, 2016—2021 [EB/OL]. (2018-01-15)[2023-11-15]. https://virtualization.network/Resources/Whitepapers/0b75cf2e-0c53-4891-918e-b542a5d364c5_white-paper-c11-738085.pdf.

[21] Le Duc T, Leiva R G, Casari P, et al. Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey [J]. ACM Computing Surveys, 52(5): 94.

[22] Sze V, Chen Y H, Yang T J, et al. Efficient processing of deep neural networks: A tutorial and survey [J]. Proceedings of the IEEE, 2017, 105(12): 2295‒2329.

[23] Ananthanarayanan G, Bahl P, Bodik P, et al. Real-time video analytics: The killer app for edge computing [J]. Computer, 2017, 50(10): 58‒67.

[24] Ha K Y, Chen Z, Hu W L, et al. Towards wearable cognitive assistance [C]. Bretton Woods: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014.

[25] Cao J, Xu L Y, Abdallah R, et al. EdgeOS_H: A home operating system for Internet of everything [C]. Atlanta: 2017 IEEE 37th International Conference on Distributed Computing Systems, 2017.

[26] Dey S, Mondal J, Mukherjee A. Offloaded execution of deep learning inference at edge: Challenges and insights [C]. Kyoto: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, 2019.

[27] Xu Z C, Zhao L Q, Liang W F, et al. Energy-aware inference offloading for DNN-driven applications in mobile edge clouds [J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(4): 799‒814.

[28] Pacheco R G, Couto R S, Simeone O. Calibration-aided edge inference offloading via adaptive model partitioning of deep neural networks [C]. Montreal: ICC 2021-IEEE International Conference on Communications, 2021.

[29] Pacheco R G, Oliveira F D V R, Couto R S. Early-exit deep neural networks for distorted images: Providing an efficient edge offloading [C]. Madrid: 2021 IEEE Global Communications Conference, 2021.

[30] Banitalebi-Dehkordi A, Vedula N, Pei J, et al. Auto-split: A general framework of collaborative edge-cloud AI [C]. Singapore: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021.

[31] Asheralieva A, Niyato D, Xiong Z H. Auction-and-learning based Lagrange coded computing model for privacy-preserving, secure, and resilient mobile edge computing [J]. IEEE Transactions on Mobile Computing, 2023, 22(2): 744‒764.

[32] Shyuan J, Lim W, Xiong Z H, et al. A double auction mechanism for resource allocation in coded vehicular edge computing [J]. IEEE Transactions on Vehicular Technology, 2022, 71(2): 1832‒1845.

[33] Lu Y, Shu Y C, Tan X, et al. Collaborative learning between cloud and end devices: An empirical study on location prediction [C]. Washington DC: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019.

[34] Ding C T, Zhou A, Liu Y X, et al. A cloud-edge collaboration framework for cognitive service [J]. IEEE Transactions on Cloud Computing, 2022, 10(3): 1489‒1499.

[35] Lyu Z Q, Zhang W Q, Zhang S Y, et al. DUET: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization [C]. New York: Proceedings of the ACM Web Conference, 2023.

[36] Yan Y K, Niu C Y, Gu R J, et al. On-device learning for model personalization with large-scale cloud-coordinated domain adaption [C]. Washington DC: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.

[37] Luy Z Q, Chen Z Y, Zhang S Y, et al. IDEAL: Toward high-efficiency device-cloud collaborative and dynamic recommendation system [EB/OL]. (2023-02-14)‍[2023-11-15]. https://arxiv.org/abs/2302.07335.

[38] Li Y N, Yuan H T, Fu Z, et al. ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning [C]. New York: Proceedings of the ACM Web Conference 2023, 2023.

[39] Zhou Z, Chen X, Li E, et al. Edge intelligence: Paving the last Mile of artificial intelligence with edge computing [J]. Proceedings of the IEEE, 2019, 107(8): 1738‒1762.

[40] Nguyen D V, Tran H T T, Thang T C. A delay-aware adaptation framework for cloud gaming under the computation constraint of user devices [C]. Thessaloniki: International Conference on Multimedia Modeling, 2019.

[41] Basiri M, Rasoolzadegan A. Delay-aware resource provisioning for cost-efficient cloud gaming [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(4): 972‒983.

[42] Kassir S, de Veciana G, Wang N N, et al. Joint update rate adaptation in multiplayer cloud-edge gaming services: Spatial geometry and performance tradeoffs [C]. New York: Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 2021.

[43] Dionisio J D N, Gilbert R. 3D Virtual worlds and the metaverse: Current status and future possibilities [J]. ACM Computing Surveys, 45(3): 34.

[44] Lee L K, Braud T, Zhou P Y, et al. All one needs to know about Metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda [EB/OL]. (2021-10-06)[2023-11-15]. https://arxiv.org/abs/2110.05352.

[45] Jakob N. Usability Engineering [M]. San Francisco: Morgan Kaufmann Publishers Inc., 1994.

[46] Bao G M, Guo P. Federated learning in cloud-edge collaborative architecture: Key technologies, applications and challenges [J]. Journal of Cloud Computing, 2022, 11(1): 94.

[47] Zhou X, Lei X Y, Yang C, et al. Handling data heterogeneity in federated learning via knowledge fusion [EB/OL]. (2022-07-23)[2023-11-15]. https://arxiv.org/abs/2207.11447.

[48] Li T, Sahu AK, Zaheer M, et al. Federated optimization in heterogeneous networks [C]. Austin: Conference on Machine Learning and Systems, 2020.

[49] Ye M, Fang X W, Du B, et al. Heterogeneous federated learning: State-of-the-art and research challenges [J]. ACM Computing Surveys, 56(3): 79.

[50] Sun Z T, Kairous P, Suresh AT, et al. Can you really backdoor federated learning? [EB/OL] (2019-11-18)[2023-11-15]. https://arxiv.org/abs/1911.07963.

[51] Lyu L J, Yu H, Ma X J, et al. Privacy and robustness in federated learning: Attacks and defenses [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022: 1‒21.

[52] Chen Y, Gui Y J, Lin H, et al. Federated learning attacks and defenses: A survey [C]. Osaka: 2022 IEEE International Conference on Big Data, 2022.

[53] Kulkarni V, Kulkarni M, Pant A. Survey of personalization techniques for federated learning [C]. London: 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 2020.

[54] Marfoq O, Neglia G, Vidal R, et al. Personalized federated learning through local memorization [C]. London: Proceedings of the 39th International Conference on Machine Learning, 2022.

[55] Pillutla K, Malik K, Mohamed A, et al. Federated learning with partial model personalization [EB/OL]. (2022-04-18)[2023-11-15]. https://arxiv.org/abs/2204.03809.

[56] Chen Z Y, Yao J C, Wang F, et al. Mc2-sf: Slow-fast learning for mobile-cloud collaborative recommendation [EB/OL]. (2021-09-25)[2023-11-15]. https://arxiv.org/abs/2109.12314.

[57] Kahneman D. Thinking, fast and slow [M]. London: Penguin Books Ltd., 2011.

[58] Madan K, Ke R N, Goyal A, et al. Fast and slow learning of recurrent independent mechanisms [EB/OL]. (2021-05-18)[2023-11-15]. https://arxiv.org/abs/2105.08710.

[59] Zhang S Y, Feng F L, Kuang K, et al. Personalized latent structure learning for recommendation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 10285‒10299.

[60] Gan Y L, Pan M J, Zhang R Y, et al. Cloud-device collaborative adaptation to continual changing environments in the real-world [C]. Vancouver: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.

[61] Qian X F, Xu Y, Lv F Y, et al. Intelligent request strategy design in recommender system [C]. Washington DC: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.

[62] Ding Y C, Niu C Y, Wu F, et al. DC-CCL: Device-cloud collaborative controlled learning for large vision models [EB/OL]. (2023-03-18)[2023-11-15]. https://arxiv.org/abs/2303.10361.

[63] Lyu C F, Niu C Y, Gu R J, et al. Walle: An end-to-end, general-purpose, and large-scale production system for device-cloud collaborative machine learning [EB/OL]. (2022-05-30)[2023-11-15]. https://arxiv.org/abs/2205.14833.

[64] Zhang S Y, Jiang T, Wang T, et al. DeVLBert: Learning deconfounded visio-linguistic representations [C]. Seattle: Proceedings of the 28th ACM International Conference on Multimedia, 2020.

[65] Zhang S Y, Feng X S, Fan W J, et al. Video audio domain generalization via confounder disentanglement [C]. Washington DC: Proceedings of the AAAI Conference on Artificial Intelligence, 2023.

[66] Zhang S Y, Jiang Z Q, Yao J C, et al. Causal distillation for alleviating performance heterogeneity in recommender systems [J]. IEEE Transactions on Knowledge and Data Engineering, 2023: 1‒16.

[67] Naghiaei M, Rahmani H A, Deldjoo Y. CPFair: Personalized consumer and producer fairness re-ranking for recommender systems [C]. Madrid: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.

[68] Huang H L, Wei S, Feng Y H, et al. Active client selection for clustered federated learning [J/OL]. IEEE Transactions on Neural Networks and Learning Systems, [2023-07-28]. https://doi.org/10. 1109/TNNLS.2023.3294295.

[69] Yang Q. Federated recommendation systems [C]. Los Angeles: 2019 IEEE International Conference on Big Data, 2019.

[70] Liu S S, Liu L K, Tang J, et al. Edge computing for autonomous driving: Opportunities and challenges [J]. Proceedings of the IEEE, 2019, 107(8): 1697‒1716.

[71] Deng Y, Zhang T H, Lou G N, et al. Deep learning-based autonomous driving systems: A survey of attacks and defenses [J]. IEEE Transactions on Industrial Informatics, 2021, 17(12): 7897‒7912.

[72] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [EB/OL]. (2014-09-04)[2023-11-15]. https://arxiv.org/abs/1409.1556.

[73] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [C]. Las Vegas: 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[74] Chen Q Y, Wang Z, Su Y, et al. Educational 5G edge computing: Framework and experimental study [J]. Electronics, 2022, 11(17): 2727.

[75] Wang C, Wang D. Managing the integration of teaching resources for college physical education using intelligent edge-cloud computing [J]. Journal of Cloud Computing, 2023, 12(1): 82.

[76] Caines A, Benedetto L, Taslimipoor S, et al. On the application of large language models for language teaching and assessment technology [EB/OL]. (2023-07-17)[2023-11-15]. https://arxiv.org/abs/2307.08393.

[77] Qian C, Yu Y, Zhou Z H. Subset selection by Pareto optimization [C]. Montréal: Advances in neural information processing systems, 2015.

[78] Gronauer S, Diepold K. Multi-agent deep reinforcement learning: A survey [J]. Artificial Intelligence Review, 2022, 55(2): 895‒943.

[79] Wu F, Souza A, Zhang T Y, et al. Simplifying graph convolutional networks [C]. Long Beach: Proceedings of the 36th International Conference on Machine Learning, 2019.