The Upsides of Turbulence: Baselining Gossip Learning in Dynamic Settings

In dynamic settings, fully distributed gossip-based learning schemes have recently gained interest due to their better scalability, robustness, and enhanced privacy protection compared to server-based architectures. However, existing approaches to their performance characterization either assume stable connectivity among nodes or are ad-hoc for specific trace-based mobility patterns. Thus, in dynamic settings, there is currently a poor understanding of the conditions under which gossip-based learning schemes are feasible, and of their main performance tradeoffs. In this work, we start addressing this issue by performing a first baselining of Gossip Learning (GL) on random Time-Varying Graphs (TVG), to get a first-order characterization of their main performance patterns in dynamic settings. The use of random TVG enables a fine-grained and accurate characterization of GL effectiveness as a function of the main system parameters while abstracting from scenariospecific features of patterns of communication and mobility (e.g., induced by road grids or measured mobility traces). Our results suggest that GL schemes are robust to node mobility and comparable in accuracy and convergence speed to Federated Learning architectures, over a wide range of operational conditions. We show that the final model accuracy is robust against data dispersion across nodes as well as against very low rates of exchanges across nodes

Gossip learning of personalized models for vehicle trajectory prediction

Gossip Learning (GL) is a peer-to-peer machine learning protocol based on direct, opportunistic exchange of models among nodes via wireless D2D communications, and on collaborative model training, which has recently proven to scale efficiently to large numbers of nodes, and to offer better privacy guarantees than traditional centralized learning architectures. Existing approaches to GL are however limited to scenarios in which nodes are static, or in which the node connectivity graph is fully connected, and they are fragile to node churn as well as to any change in network configuration. To overcome this limitation, we present a new decentralized architecture for GL suitable for setups with dynamic nodes, which benefits from node mobility instead of being hampered by it. In our approach, nodes improve their personalized model instance by sharing it with neighbors, and by weighting neighbors' contributions according to an estimate of their marginal utility. We apply our GL algorithm to short-term vehicular trajectory estimation in realistic urban scenarios. We propose a new strategy for the estimation of the neighbors' instances marginal utility, which yields satisfactory trajectory estimation accuracy for nodes with long enough sojourn times

Poster ::mobile gossip learning for trajectory rediction

Gossip Learning (GL) is a fully decentralized machine learning paradigm with the potential to enable highly scalability and to preserve user privacy. The majority of existing results however consider scenarios in which either each node communicates with all other nodes, or in which the connectivity graph is static, and they are therefore inapplicable in dynamic setups such as in VANETs. This work is a first attempt at designing and assessing GL schemes suited for scenarios with moving nodes with the application of predicting the trajectory of moving cars

A gossip learning approach to urban trajectory nowcasting for anticipatory RAN management

In future radio access networks, machine learning (ML) based strategies for short-term forecasting of vehicular trajectories will be key for anticipatory resource allocation and management at the mobile edge. However, training ML models in a centralized fashion, over data collected from a massive heterogeneous and dynamic set of devices, poses significant scalability, reliability, and efficiency challenges, which are still open to date. In this paper, we look at the specific issue of scalable and resource-efficient training of ML models in a vehicular environment. To address such a challenge, we propose a new Gossip Learning scheme, i.e., a fully distributed, collaborative training approach based on direct, opportunistic model exchanges via wireless device-to-device (D2D) communications with no centralized support. Our approach is based on constantly improving each node's own model instance through knowledge transfer among nodes, and on different strategies for estimating the potential contribution of neighboring nodes to the training process at a node. Extensive numerical assessments on a variety of measurement-based dynamic urban scenarios suggest that our schemes are able to converge rapidly and provide sufficiently accurate forecasts of vehicle position for time horizons which are typical of future 5G/6G dynamic resource allocation algorithms

ABIDI: A Reference Architecture for Reliable Industrial Internet of Things

info:eu-repo/semantics/publishe

ABIDI ::a reference architecture for reliable industrial Internet of things

The rationale behind the ever increasing combined adoption of Artificial Intelligence and Internet of Things (IoT) technologies in the industry lies in its potential for improving resource efficiency of the manufacturing process, reducing capital and operational expenditures while minimizing its carbon footprint. Nonetheless, the synergetic application of these technologies is hampered by several challenges related to the complexity, heterogeneity and dynamicity of industrial scenarios. Among these, a key issue is how to reliably deliver target levels of data quality and veracity, while effectively supporting a heterogeneous set of applications and services, ensuring scalability and adaptability in dynamic settings. In this paper we perform a first step towards addressing this issue. We outline ABIDI, an innovative and comprehensive Industrial IoT reference architecture, enabling context-aware and veracious data analytics, as well as automated knowledge discovery and reasoning. ABIDI is based on the dynamic selection of the most efficient IoT, networking and cloud/edge technologies for different scenarios, and on an edge layer that efficiently supports distributed learning, inference and decision making, enabling the development of real-time analysis, monitoring and prediction applications. We exemplify our approach on a smart building use case, outlining the key design and implementation steps which our architecture implies