This paper studies the performance of Floating Gossip, a novel decentralized approach for Gossip Learning at the network edge. Floating Gossip utilizes Floating Content to facilitate location-based probabilistic evolution of Machine Learning models, without external infrastructure support. We investigate dynamic scenarios requiring continuous learning, leveraging a mean field approach to analyze Floating Gossip's performance boundaries. Our focus is on the quantity of data that users can integrate into their models, as a function of key system parameters. Unlike previous studies that separately optimize communication or computational aspects of Gossip Learning, our methodology considers their combined effect. We validate our analysis through comprehensive simulations, demonstrating the high accuracy of our analytical model. Our methodology reveals Floating Gossip's effectiveness in training and updating Machine Learning models collaboratively, leveraging opportunistic exchanges between mobile users, while flexibly adapting to different user characteristics and mobility patterns. This research highlights Floating Gossip's potential for continuous, cooperative model training in dynamic, infrastructure-less environments, offering insight into its performance patterns and its potential in practical applications.

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.

Floating Content (FC) is a paradigm for localized infrastructure-less content dissemination, that aims at sharing information among nodes within a restricted geographical area by relying only on opportunistic content exchanges. FC provides the basis for the probabilistic spatial storage of shared information in a completely decentralized fashion, usually without support from dedicated infrastructure. One of the key open issues in FC is the characterization of its performance limits as functions of the system parameters, accounting for its reliance on volatile wireless exchanges and on limited user resources. This paper takes a first step towards tackling this issue, by elaborating a model for the storage capacity of FC, i.e., for the maximum amount of information that can be stored through the FC paradigm. The storage capacity of FC, and of similar probabilistic content dissemination systems, is evaluated with a powerful information theoretical approach, based on a mean field model of opportunistic information exchange. In addition, an extremely simple explicit approximate expression for storage capacity is derived. The numerical results generated by our analytical models are compared to the predictions of realistic simulations under different setups, proving the accuracy of our analytical approaches, and characterizing the properties of the FC storage capacity.

Floating Content (FC) is a paradigmatic example of opportunistic infrastructure-less content sharing system where information is spread upon mobile node encounters within an area which is called the replication zone. FC allows the probabilistic spatial storage of information, even in the case of unreliable communications, with no support from dedicated servers. Given the large amount of communication and storage resources typically required to guarantee content persistence despite node mobility, a major open issue for the practical viability of FC and of similar distributed storage systems is the characterization of their storage capacity, i.e., of the maximum amount of information which can be stored for a given set of system parameters. In this paper, we propose a simple yet powerful information theoretical model of the storage capacity of probabilistic distributed storage systems such as FC, based on a mean field model of opportunistic information exchange. We evaluate numerically our results, and validate the model by means of realistic simulations, showing the accuracy of our mean field approach and characterizing the properties of the FC storage capacity versus the main system parameters.

Information sharing is becoming a relevant issue for mobile broadband operators, due to the increasing popularity of social networks, to the increasing volumes of shared information, and to the steady increase in the number and capabilities of mobile devices connected to the Internet. Offloading information sharing services from the cellular infrastructure to device-to-device (D2D) communications can offer a welcome reduction of traffic. This paper discusses experiments with a smartphone information sharing application that can be used on commercial-off-the-shelf devices, with no need to root the device's software. In order to avoid unrealistic assumptions on the behavior of D2D communications, this work includes and builds upon the implementation of an Android application that supports infras-tructureless distributed content sharing among wireless devices using Wi-Fi Direct. The collected experimental data permit a detailed analysis of the occurring events, and a careful assessment of the performance of pervasive information sharing services. Our experiments reveal that many assumptions commonly used in the literature do not hold in real settings. We conclude that delay-tolerant services can be supported, albeit we also show that high densities of devices can (somewhat counter-intuitively) impair performance.