Aiming at the problems of easily falling into local optimum， slow convergence and low solution accuracy of standard Slime Mould Algorithm （SMA）， an Improved Slime Mould Algorithm with Multi-Strategy fusion （MSISMA） was proposed. Firstly， Brownian motion and Levy flight were introduced to enhance the search ability of the algorithm. Secondly， according to different stages of the algorithm， the location update formula of the slime mould was improved to increase the convergence speed and accuracy of the algorithm. Thirdly， the Interval Adaptative Opposition-Based Learning （IAOBL） strategy was adopted to generate the reverse population， with which the diversity and quality of the population were improved， as a result， the convergence speed of the algorithm was improved. Finally， a convergence stagnation monitoring strategy was introduced， which would make the algorithm jump out of the local optimum by re-initializing the positions of some slime mould individuals. With 23 test functions selected，the proposed MSISMA was tested and compared with Equilibrium Slime Mould Algorithm （ESMA）， Slime Mould Algorithm combined to Adaptive Guided Differential Evolution Algorithm （SMA-AGDE）， SMA， Marine Predators Algorithm （MPA） and Equilibrium Optimizer （EO）. Moreover， the Wilcoxon rank-sum test was performed on the running results of all algorithms. Compared with the above algorithms， MSISMA achieves the best average value on 19 test functions and the best standard deviation on 12 test functions， and has the optimization accuracy improved by 23.39% to 55.97% on average. Experimental results show that the convergence speed， solution accuracy and robustness of MSISMA are significantly better.

Under the background of emphasizing data right confirmation and privacy protection， federated learning， as a new machine learning paradigm， can solve the problem of data island and privacy protection without exposing the data of all participants. Since the modeling methods based on federated learning have become mainstream and achieved good effects at present， it is significant to summarize and analyze the concepts， technologies， applications and challenges of federated learning. Firstly， the development process of machine learning and the inevitability of the appearance of federated learning were elaborated， and the definition and classification of federated learning were given. Secondly， three federated learning methods （including horizontal federated learning， vertical federated learning and federated transfer learning） which were recognized by the industry currently were introduced and analyzed. Thirdly， concerning the privacy protection issue of federated learning， the existing common privacy protection technologies were generalized and summarized. In addition， the recent mainstream open-source frameworks were introduced and compared， and the application scenarios of federated learning were given at the same time. Finally， the challenges and future research directions of federated learning were prospected.