In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Gossip Learning scheme which relies on Floating Content to implement location-based probabilistic evolution of machine learning models in an infrastructure-less manner. We consider dynamic scenarios where continuous learning is necessary, and we adopt a mean field approach to investigate the limit performance of Floating Gossip in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Different from existing approaches in which either communication or computing aspects of Gossip Learning are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, based on opportunistic exchanges among moving users.

翻译：本文研究了浮动八卦（Floating Gossip）的极限性能，这是一种新型的全分布式八卦学习方案，它依赖浮动内容以无基础设施的方式实现位置相关的机器学习模型概率演化。我们考虑需要持续学习的动态场景，采用平均场方法探究浮动八卦在极限性能方面，用户可将数据纳入其模型的数据量随主要系统参数变化的情况。与现有方法分别分析或优化八卦学习的通信或计算方面不同，我们的方法同时考虑两者的综合影响。通过详细模拟验证了结果，证明其具有良好的准确性。我们提出的模型表明，浮动八卦能够基于移动用户间的机会性交换，以协作方式高效实现机器学习模型的持续训练与更新。