human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "performance analysis and optimization in privacy preserving federated learning"):
  Abstract: As a means of decentralized machine learning, federated learning (FL) has recently drawn considerable attentions. One of the prominent advantages of FL is its capability of preventing clients' data from being directly exposed to external adversaries. Nevertheless, via a viewpoint of information theory, it is still possible for an attacker to steal private information from eavesdropping upon the shared models uploaded by FL clients. In order to address this problem, we develop a novel privacy preserving FL framework based on the concept of differential privacy (DP). To be specific, we first borrow the concept of local DP and introduce a client-level DP (CDP) by adding artificial noises to the shared models before uploading them to servers. Then, we prove that our proposed CDP algorithm can satisfy the DP guarantee with adjustable privacy protection levels by varying the variances of the artificial noises. More importantly, we derive a theoretical convergence upper-bound of the CDP algorithm. Our derived upper-bound reveals that there exists an optimal number of communication rounds to achieve the best convergence performance in terms of loss function values for a given privacy protection level. Furthermore, to obtain this optimal number of communication rounds, which cannot be derived in a closed-form expression, we propose a communication rounds discounting (CRD) method. Compared with the heuristic searching method, our proposed CRD can achieve a much better trade-off between the computational complexity of searching for the optimal number and the convergence performance. Extensive experiments indicate that our CDP algorithm with an optimization on the number of communication rounds using the proposed CRD can effectively improve both the FL training efficiency and FL model quality for a given privacy protection level.

Includes members:
  Member "advances and open problems in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "on safeguarding privacy and security in the framework of federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey")
  Member "federated reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated hierarchical hybrid networks for clickbait detection")
  Member "deep leakage from gradients":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "federated learning with differential privacy algorithms and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless federated learning with local differential privacy")
  Member "the value of collaboration in convex machine learning with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting performance of asynchronous differentially private learning")
  Member "federated learning challenges methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "age based scheduling policy for federated learning in mobile edge networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "update aware device scheduling for federated learning at the wireless edge")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)