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Research Topics in Multi-Objective Evolutionary Federated Learning

Research Topics in Multi-Objective Evolutionary Federated Learning

  Accumulation of data in the real world increases, leading to data security and privacy problems. Federated learning (FL) aims to learn the model by collecting the data through different mediums and training the information from distributed networks. Federated learning is the developing technique used to prevent private information leakage and found in a wide range of applications due to its data privacy concern. The importance of federated learning is the capability of addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data.

  Evolutionary algorithms are the heuristic-based approach for solving the problem with multinomial data and provide the optimum solution by self adapt search in large neural networks. The importance of federated learning with evolutionary algorithm helps in handling data distribution in user side and gradients during learning. Federate learning involves data collection from various users to build a common model. Hence, there is a need for multi or many-objective evolutionary federated learning for better optimization.

 The optimization using the multi-objective evolutionary algorithm in federated learning aims to minimize the communication cost and maximize the performance of the learning model. The neural networks utilized for multi-objective evolutionary federated learning are multilayer perceptrons and convolutional neural networks.