Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos. However, the conventional FL approach has two major limitations. First, the heterogeneous data on individual silos can cause the global model to perform well for some clients but not all, as the update direction on some clients may hinder others after they are aggregated. Second, it is lacking with respect to the efficiency perspective concerning communication costs during FL and large model sizes. This paper proposes a new technical solution that utilizes network pruning on client models and aggregates the pruned models. This method enables local models to be tailored to their respective data distribution and mitigate the data heterogeneity present in agri-food data. Moreover, it allows for more compact models that consume less data during transmission. We experiment with a soybean yield forecasting dataset and find that this approach can improve inference performance by 15.5% to 20% compared to FedAvg, while reducing local model sizes by up to 84% and the data volume communicated between the clients and the server by 57.1% to 64.7%. Our method demonstrates the potential to use efficient models that are more environmentally friendly to support the agri-food sector’s transition to net zero. Future enhancements of this method could further optimize distributed learning in agri-food, enhancing sustainability and applicability.

Securing provenance has recently become an important research topic, resulting in a number of models for protecting access to provenance. Existing work has focused on graph transformation mechanisms that supply a user with a provenance view that satisfies both access control policies and validity constraints of provenance. However, it is not always possible to satisfy both of them simultaneously, because these two conditions are often inconsistent which require sophisticated conflict resolution strategies to be put in place. In this paper we develop a new access control model tailored for provenance. In particular, we explicitly take into account validity constraints of provenance when specifying certain parts of provenance to which access is restricted. Hence, a provenance view that is granted to a user by our authorisation mechanism would automatically satisfy the validity constraints. Moreover, we propose algorithms that allow provenance owners to deploy fine-grained access control for their provenance data.

Systems for allowing users to manage access to their personal data are important for a wide variety of applications including healthcare, where authorised individuals may need to share information in ways that the owner had not anticipated. Simply denying access in unknown cases may hamper critical decisions and affect service delivery. Rather, decisions can be made considering the risk of a given sharing request, and the trustworthiness of the requester. We propose a trust- and risk-aware access control mechanism (TRAAC) and a sparse zone-based policy model, which together allow decision-making on the basis of the requester's trustworthiness with regards to both the information to be shared, and the completion of obligations designed to mitigate risk. We formalise our approach and compare it with an existing approach that does not model trust through simulation.

The ourSpaces Virtual Research Environment makes use of Semantic Web technologies to create a platform to support multi-disciplinary research groups. This paper introduces the main semantic components of the system: a framework to capture the provenance of the research process, a collection of services to create and visualise metadata and a policy reasoning service. We also describe different approaches to authoring and accessing metadata within the VRE. Using evidence gathered from data provided by the users of the system we discuss the lessons learnt from deployment with three case study groups.

Assessing the quality of sensor data available on the Web is essential in order to identify reliable information for decision-making. This paper discusses how provenance of sensor observations and previous quality ratings can influence quality assessment decisions.

Web-based Virtual Research Environments (VREs) have been proposed as one way in which e-Science tools can be deployed to support and enhance the research process. We are exploring the use of Linked Data in combination with the Open Provenance Model (OPM) and Social Web concepts to facilitate interactions between people and data in the context of a VRE. In this demo we present the ourSpaces VRE and outline the technologies used to link together provenance, research artefacts, projects, geographical locations and social data in the context of interdisciplinary research.

In this paper we explore the use of proposed semantic grid standards and methodology through deployment of a land use modelling service. The FEARLUS-G service architecture is presented which allows large scale simulation experiments to be distributed over the grid. We also discuss ontology support for simulation parameters, hypotheses and results that facilitates sharing and re-use of such resources among land-use scientists. This leads to a description of infrastructure for semantic data management which integrates Jena2 and the ELDAS data access service.

Interface agents are being developed to assist users with a variety of tasks. To perform effectively, such agents need knowledge of user preferences. An agent architecture has been developed which observes a user performing tasks, and identifies features which can be used as training data by a learning algorithm. Using the learned profile, an agent can give advice to the user on dealing with new situations. The architecture has been applied to two different information filtering domains: classifying incoming mail messages (Magi) and identifying interesting USENet news articles (UNA). This paper describes the architecture and examines the results of experimentation with different learning algorithms and different feature extraction strategies within these domains.