Applying machine learning to fully distributed environments is always becoming more crucial in several real-life contexts. In networked environments, data models can evolve dynamically over time subject to continuous changes known as concept drifts. Detecting when concept drift occurs is essential for various drift-handling techniques and plays a significant role in many scenarios. However, while drift-handling methods exist, an efficient solution for detecting drift in large-scale networks remains unknown. This study proposes a concept drift detection approach allowing to capture temporal variations in the data, enabling a more robust identification of data changes. Specifically, our method detects local models exhibiting a concept drifts by analyzing deviations in learned representations over time. We validate our approach using a real-world urban traffic dataset, demonstrating its effectiveness in identifying concept drift in real scenarios. The results show that the proposed approach successfully identifies sudden and gradual drifts, respectively achieving an F1-score of 0.94 in sudden drift detection and an F1-score of 0.92 in gradual drift detection.

The exponential growth of the Internet of Things (IoT) has generated vast amounts of data at the edge, creating challenges for privacy-preserving and resource-efficient machine learning. Federated Learning (FL) has emerged as a decentralized paradigm that trains models locally on edge devices, reducing privacy risks and communication overhead. However, FL faces significant challenges related to data and resource heterogeneity across devices, including disparities in computational power, memory, and network. To address these issues, this study introduces AH-Fed, an adaptive framework for Heterogeneous Federated Learning (HFL). AH-Fed employs a tri-tiered model architecture to accommodate diverse device capabilities to minimize communication overhead, and stratified resource management to ensure fair and efficient device participation. By adapting training strategies based on device-specific constraints, AH-Fed enhances the inclusivity, scalability, and efficiency of FL systems. Empirical validation demonstrates the framework’s effectiveness in optimizing resource utilization and improving model performance in real-world scenarios.

The adoption of machine learning in distributed environments is expanding rapidly, providing substantial benefits for real-time decision-making. However, this evolution has also increased the prevalence of poisoning attacks that target the model training phase.Defending against such threats is crucial, yet existing defense mechanisms often overlook the dynamic nature of data in evolving contexts. To address this gap, we propose a novel defense strategy that leverages concept drift analysis within a spatiotemporal framework based on a transformer autoencoder. This approach captures both spatial correlations and temporal variations in the data, enabling a more robust identification of adversarial patterns. Specifically, our method detects and excludes compromised local models exhibiting adversarial behavior by analyzing deviations in learned representations over time.We validate our approach using a real-world urban traffic dataset, demonstrating its effectiveness in mitigating the impact of poisoning attacks in dynamic, real-world scenarios. The results confirm that our method successfully identifies poisoned stations, achieving an F1 score of 0.9 in local drift detection. Furthermore, by dynamically filtering out adversarial drifts, our approach enhances the underlying model's robustness against poisoning attacks.

Abstract Blockchain is a cross‐cutting technology allowing interactions among untrusted entities in a distributed manner without the need for involving a trusted third party. Smart contracts (i.e., programs running on the blockchain) enabled organizations to envision and implement solutions to real‐world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. With the help of an exploratory survey involving developers and researchers, this paper identifies the practices and tools used to develop, implement, and evaluate smart contracts. The survey received 55 valid responses. Such responses indicate that (i) inefficiencies may occur during the development cycle of a smart contract, especially regarding requirements specification, design, and testing phases, and (ii) the lack of a shared standard to evaluate the functional quality of implemented smart contracts. To start coping with these issues, the adoption of functional suitability assessment measures recommended by the ISO/IEC 25000 standard, widely used in software engineering, is proposed by adapting them to the context of smart contracts. Through some examples, the manuscript also illustrates how to measure the functional completeness and correctness of smart contracts. The proposed procedure to measure smart contract functional suitability brings advantages to both developers and users of decentralized finance or non‐fungible tokens platforms, data marketplaces, or shipping and real estate services, just to mention a few. In particular, it helps (i) better outline the responsibilities of smart contracts, (ii) uncover errors and deficiencies of smart contracts in the early stages, and (iii) ensure that the established requirements are met.

Blockchain technology is becoming increasingly popular, and smart contracts (i.e., programs that run on top of the blockchain) represent a crucial element of this technology. In particular, smart contracts running on Ethereum (i.e., one of the most popular blockchain platforms) are often developed with Solidity, and their deployment and execution consume gas (i.e., a fee compensating the computing resources required). Smart contract development frequently involves code reuse, but poor readable smart contracts could hinder their reuse. However, writing readable smart contracts is challenging, since practices for improving the readability could also be in contrast with optimization strategies for reducing gas consumption. This paper aims at better understanding (i) the readability aspects for which traditional software and smart contracts differ, and (ii) the specific smart contract readability features exhibiting significant relationships with gas consumption. We leverage a set of metrics that previous research has proven correlated with code readability. In particular, we first compare the values of these metrics obtained for both Solidity smart contracts and traditional software systems (written in Java). Then, we investigate the correlations occurring between these metrics and gas consumption and between each pair of metrics. The results of our study highlight that smart contracts usually exhibit lower readability than traditional software for what concerns the number of parentheses, inline comments, and blank lines used. In addition, we found some readability metrics (such as the average length of identifiers and the average number of keywords) that significantly correlate with gas consumption.

Blockchain is increasingly revolutionizing a variety of sectors, from finance to healthcare. Indeed, the availability of public blockchain platforms, such as Ethereum, has stimulated the development of hundreds of decentralized apps (dApps) that combine smart contract(s) and a front-end user interface. Smart contracts are software, as well, and, as traditional software, they require to be developed and maintained or evolved. Among all the quality properties that must be assessed and guaranteed, readability is a key aspect of source code: a highly readable code facilitates its maintainability, portability, and reusability. This is especially true when considering smart contracts, where code reuse is widely adopted. Indeed, smart contract developers often integrate code portions from other smart contracts in their artifacts. To help developers and researchers more easily estimating and monitoring the code readability of smart contracts, in this demo, we present iSCREAM. iSCREAM automatically inspects Solidity smart contracts and computes a set of metrics that previous research demonstrated being related to code readability. We evaluated iSCREAM on 90 real-world smart contract functions, showing that our tool correctly computes all the aforementioned metrics. Demo webpage: https://github.com/mfredella/iSCREAM