publications

2026

1. ITADATA

   Experimenting with Federated Learning for Multivariate Time Series Classification

   Bruno Casella, and Lorenzo Sciandra

   In The 5th Italian Conference on Big Data and Data Science, 2026

   Abs

   Time Series Classification (TSC) is a pivotal task in modern machine learning, with widespread applications in sensitive domains such as healthcare, finance, and cybersecurity. In real-world scenarios, data are inherently distributed and cannot be centralized due to strict privacy regulations, making federated learning a natural and necessary paradigm for collaborative model training. While several TSC methods have achieved strong performance in centralized settings, their gradient-based nature often results in prohibitive communication costs and limited efficiency when deployed in federated environments. In this context, MiniFed2RC has recently emerged as a fully federated, gradient-free approach for univariate TSC, combining random Rocket kernels with a closed-form ridge regression classifier. It has demonstrated state-of-the-art accuracy on the UCR univariate benchmark while significantly reducing computation and communication overhead. However, its effectiveness on multivariate time series—which are increasingly common in practice—has not yet been explored. In this paper, we present the first comprehensive benchmark of MiniFed2RC on the UEA multivariate time series classification archive. We evaluate the method across all 30 datasets, analyzing its predictive performance and communication costs. Our results show that MiniFed2RC achieves competitive classification performance on multivariate data, maintaining the same efficiency and robustness observed in the univariate setting. Code is available at https://github.com/CasellaJr/Fed2RC.

2. DAMI

   Fast Federated Time Series Classification using Rocket Kernels and Closed-form Classifiers

   Bruno Casella, Samuele Fonio, Lorenzo Sciandra, and 4 more authors

   Data Mining and Knowledge Discovery, 2026

   DOI
3. ICANN

   A Lightweight Random Feature Framework for Efficient Image Classification

   Luca Grandi, Loris Signoretti, Lorenzo Sciandra, and 3 more authors

   In 35th International Conference on Artificial Neural Networks – ICANN 2026, 2026

   DOI

2025

1. WAFL@ECML

   Personalized Aggregation for Federated Prototypical Learning

   Samuele Fonio, Bruno Casella, and Marco Aldinucci

   In Proceedings of the ECML-PKDD Workshop, 3nd workshop on advancements in Federated Learning, Porto, Portugal, 2025

   Abs

   Federated Learning (FL) systems face numerous challenges, including data heterogeneity, communication constraints, and vulnerability to adversarial attacks. A promising approach that has recently gained attention is prototype learning, which gives the possibility to share only local prototypes rather than entire models. However, the reliability of such systems is often overlooked, leading to insufficient attention to potential attack vectors. In this paper, we investigate the robustness of two Federated Prototypical methods under both random and revert poisoning attacks. We propose an extension of these methods using personalized aggregation in two different ways to enhance their resilience. Our approach provides valuable insights into the reliability of Federated Prototype Learning and contributes to the development of more robust FL systems. We validate our findings through an extensive experimental evaluation on Cifar-10 and EMNIST, with gains in performance up to 10% and 20%, respectively, with respect to the baselines.

2. ECAI

   Fed2RC: Federated Rocket Kernels and Ridge Classifier for Time Series Classification

   Bruno Casella, Samuele Fonio, Lorenzo Sciandra, and 4 more authors

   In The European Conference on Artificial Intelligence, ECAI 2025, Bologna, Italy, October 25-30, 2025, 2025

   DOI
3. WAFL@ECML

   Decentralized Time Series Classification with ROCKET Features

   Bruno Casella, Matthias Jakobs, Marco Aldinucci, and 1 more author

   In Proceedings of the ECML-PKDD Workshop, 3nd workshop on advancements in Federated Learning, Porto, Portugal, 2025

   Abs

   Time series classification (TSC) is a critical task with applications in various domains, including healthcare, finance, and industrial monitoring. Due to privacy concerns and data regulations, Federated Learning has emerged as a promising approach for learning from distributed time series data without centralizing raw information. However, most FL solutions rely on a client-server architecture, which introduces robustness and confidentiality risks related to the distinguished role of the server, which is a single point of failure and can observe knowledge extracted from clients. To address these challenges, we propose DROCKS, a fully decentralized FL framework for TSC that leverages ROCKET (RandOm Convolutional KErnel Transform) features. In DROCKS, the global model is trained by sequentially traversing a structured path across federation nodes, where each node refines the model and selects the most effective local kernels before passing them to the successor. Extensive experiments on the UCR archive demonstrate that DROCKS outperforms state-of-the-art client-server FL approaches while being more resilient to node failures and malicious attacks. Our code is available at https://anonymous.4open.science/r/DROCKS-7FF3/README.md.

4. ACCESS

   Cloud Detection Challenge-Methods and Results

   Alessio Barbaro Chisari, Luca Guarnera, Alessandro Ortis, and 7 more authors

   IEEE Access, 2025

   DOI
5. PDP

   A performance analysis of VM-based Trusted Execution Environments for Confidential Federated Learning

   Bruno Casella

   In 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025, Turin, Italy, March 12-14, 2025, 2025

   DOI

2024

1. ESANN

   Federated Learning in a Semi-Supervised Environment for Earth Observation Data

   Bruno Casella, Alessio Barbaro Chisari, Marco Aldinucci, and 2 more authors

   In Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, 2024

   Abs DOI

   We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor of a classification model via weight averaging. The fully connected layers of the supervised models are also averaged in a federated fashion. We show the effectiveness of our approach by comparing it with the state-of-the-art federated algorithm, an isolated and a centralised baseline, on novel cloud detection datasets.

2. ESANN

   Federated Time Series Classification with ROCKET features

   Bruno Casella, Jakobs Matthias, Marco Aldinucci, and 1 more author

   In Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, 2024

   Abs DOI

   This paper proposes FROCKS, a federated time series classification method using ROCKET features. Our approach dynamically adapts the models’ features by selecting and exchanging the best-performing ROCKET kernels from a federation of clients. Specifically, the server gathers the best-performing kernels of the clients together with the associated model parameters, and it performs a weighted average if a kernel is best-performing for more than one client. We compare the proposed method with state-of-the-art approaches on the UCR archive binary classification datasets and show superior performance on most datasets.

3. SP

   A Performance Analysis for Confidential Federated Learning

   Bruno Casella, Iacopo Colonnelli, Gianluca Mittone, and 5 more authors

   In Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024, 2024

   Abs DOI

   Federated Learning (FL) has emerged as a solution to preserve data privacy by keeping the data locally on each participant’s device. However, FL alone is still vulnerable to attacks that can cause privacy leaks. Therefore, it becomes necessary to take additional security measures at the cost of increasing runtimes. The Trusted Execution Environment (TEE) approach promises to offer the highest degree of security during execution. However, TEEs suffer from memory limits which prevent safe end-to-end FL training of modern deep models. State-of- the-art approaches limit secure training to selected layers, failing to avert the full spectrum of attacks or adopt layer-wise training affecting model performance. We benchmark the usage of a library OS (LibOS) to run the full, unmodified end-to-end FL training inside the TEE. We extensively evaluate and model the overhead of the different security mechanisms needed to protect the data and model during computation (TEE), communication (TLS), and storage (disk encryption). The obtained results across three datasets and two models demonstrate that LibOSes are a viable way to seamlessly inject security into FL with limited overhead (at most 2x), offering valuable guidance for researchers and developers aiming to apply FL in data-security-focused contexts.

4. ECML

   Federated AdaBoost for Survival Analysis

   Oussama Harrak, Bruno Casella, Samuele Fonio, and 5 more authors

   In Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements in Federated Learning, 2024

   Abs

   This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets. Our code is available at https://github.com/oussamaHarrak/FedSurvBoost.

5. ACCESS

   Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios

   Bruno Casella, Roberto Esposito, Antonio Sciarappa, and 2 more authors

   IEEE Access, 2024

   DOI
6. STAR

   Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis

   Bruno Casella, Walter Riviera, Marco Aldinucci, and 1 more author

   STAR Protocols, 2024

   https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf

   Abs DOI

   Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources.

7. CVIU

   FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis

   Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, and 4 more authors

   Computer Vision and Image Understanding, 2024

   Abs DOI

   In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a federated and decentralized learning strategy, FedER, that, exploiting experience replay and generative adversarial concepts, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. FedER is tested on two tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation. Code is available at https://github.com/perceivelab/FedER

2023

1. ICBC

   Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting

   Bruno Casella, and Lorenzo Paletto

   In Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai, 2023

   https://ieeexplore.ieee.org/document/10174989

   Abs DOI

   Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network’s activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies’ future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.

2. DML-ICC

   Architecture-Based FedAvg for Vertical Federated Learning

   Bruno Casella, and Samuele Fonio

   In Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023, 2023

   https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf

   Abs DOI

   Federated Learning (FL) has emerged as a promising solution to address privacy concerns by collaboratively training Deep Learning (DL) models across distributed parties. This work proposes an architecture-based aggregation strategy in Vertical FL, where parties hold data with different attributes but shared instances. Our approach leverages the identical architectural parts, i.e. neural network layers, of different models to selectively aggregate weights, which is particularly relevant when collaborating with institutions holding different types of datasets, i.e., image, text, or tabular datasets. In a scenario where two entities train DL models, such as a Convolutional Neural Network (CNN) and a Multi-Layer Perceptron (MLP), our strategy computes the average only for architecturally identical segments. This preserves data-specific features learned from demographic and clinical data. We tested our approach on two clinical datasets, i.e., the COVID-CXR dataset and the ADNI study. Results show that our method achieves comparable results with the centralized scenario, in which all the data are collected in a single data lake, and benefits from FL generalizability. In particular, compared to the non-federated models, our proposed proof-of-concept model exhibits a slight performance loss on the COVID-CXR dataset (less than 8%), but outperforms ADNI models by up to 12%. Moreover, communication costs between training rounds are minimized by exchanging only the dense layer parameters.

3. ICCVW

   Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning

   Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, and 4 more authors

   In Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023, 2023

   https://ieeexplore.ieee.org/document/10350429

   Abs DOI

   Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in multiple nodes, while continual learning faces with temporal aspect of training as it deals with continuous streams of data. Distribution shifts over space and time is what it happens in real federated learning scenarios that show multiple challenges. First, the federated model needs to learn sequentially while retaining knowledge from the past training rounds. Second, the model has also to deal with concept drift from the distributed data distributions. To address these complexities, we attempt to combine continual and federated learning strategies by proposing a solution inspired by experience replay and generative adversarial concepts for supporting decentralized distributed training. In particular, our approach relies on using limited memory buffers of synthetic privacy-preserving samples and interleaving training on local data and on buffer data. By translating the CL formulation into the task of integrating distributed knowledge with local knowledge, our method enables models to effectively integrate learned representation from local nodes, providing models the capability to generalize across multiple datasets.We test our integrated strategy on two realistic medical image analysis tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation.

4. Patterns

   MERGE: A model for multi-input biomedical federated learning

   Bruno Casella, Walter Riviera, Marco Aldinucci, and 1 more author

   Patterns, 2023

   Abs DOI

   Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic images. Imaging, however, is not the only source of information. Tabular data, such as personal and genomic data and blood test results, are routinely collected but rarely considered in DL pipelines. Nevertheless, DL requires large datasets that often must be pooled from different institutions, raising non-trivial privacy concerns. Federated learning (FL) is a cooperative learning paradigm that aims to address these issues by moving models instead of data across different institutions. Here, we present a federated multi-input architecture using images and tabular data as a methodology to enhance model performance while preserving data privacy. We evaluated it on two showcases: the prognosis of COVID-19 and patients’ stratification in Alzheimer’s disease, providing evidence of enhanced accuracy and F1 scores against single-input models and improved generalizability against non-federated models.

5. CCI

   Predictors of target lesion failure after treatment of left main, bifurcation, or chronic total occlusion lesions with ultrathin-strut drug-eluting coronary stents in the ULTRA registry

   Ovidio Filippo, Francesco Bruno, Tineke H. Pinxterhuis, and 31 more authors

   Catheterization and Cardiovascular Interventions, 2023

   Abs DOI

   Background: Data about the long-term performance of new-generation ultrathin-strut drug-eluting stents (DES) in challenging coronary lesions, such as left main (LM), bifurcation, and chronic total occlusion (CTO) lesions are scant. Methods: The international multicenter retrospective observational ULTRA study included consecutive patients treated from September 2016 to August 2021 with ultrathin-strut (<70µm) DES in challenging de novo lesions. Primary endpoint was target lesion failure (TLF): composite of cardiac death, target-lesion revascularization (TLR), target-vessel myocardial infarction (TVMI), or definite stent thrombosis (ST). Secondary endpoints included all-cause death, acute myocardial infarction (AMI), target vessel revascularization, and TLF components. TLF predictors were assessed with Cox multivariable analysis. Results: Of 1801 patients (age: 66.6±11.2 years; male: 1410 [78.3%]), 170 (9.4%) experienced TLF during follow-up of 3.1±1.4 years. In patients with LM, CTO, and bifurcation lesions, TLF rates were 13.5%, 9.9%, and 8.9%, respectively. Overall, 160 (8.9%) patients died (74 [4.1%] from cardiac causes). AMI and TVMI rates were 6.0% and 3.2%, respectively. ST occurred in 11 (1.1%) patients while 77 (4.3%) underwent TLR. Multivariable analysis identified the following predictors of TLF: age, STEMI with cardiogenic shock, impaired left ventricular ejection fraction, diabetes, and renal dysfunction. Among the procedural variables, total stent length increased TLF risk (HR: 1.01, 95% CI: 1-1.02 per mm increase), while intracoronary imaging reduced the risk substantially (HR: 0.35, 95% CI: 0.12-0.82). Conclusions: Ultrathin-strut DES showed high efficacy and satisfactory safety, even in patients with challenging coronary lesions. Yet, despite using contemporary gold-standard DES, the association persisted between established patient- and procedure-related features of risk and impaired 3-year clinical outcome.

6. ML4ASTRO

   Federated Learning meets HPC and cloud

   Iacopo Colonnelli, Bruno Casella, Gianluca Mittone, and 6 more authors

   In Astrophysics and Space Science Proceedings, 2023

   Keynote talk

   Abs DOI

   HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.

2022

1. ITADATA

   Benchmarking FedAvg and FedCurv for Image Classification Tasks

   Bruno Casella, Roberto Esposito, Carlo Cavazzoni, and 1 more author

   In Proceedings of the 1st Italian Conference on Big Data and Data Science, ITADATA 2022, September 20-21, 2022, 2022

   Abs

   Classic Machine Learning (ML) techniques require training on data available in a single data lake (either centralized or distributed). However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.

2. VISAPP

   Transfer Learning via Test-time Neural Networks Aggregation

   Bruno Casella, Alessio Chisari, Sebastiano Battiato, and 1 more author

   In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Volume 5: VISAPP, Online Streaming, February 6-8, 2022, 2022

   Abs DOI

   It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as in a new (testing) set drawn from a different distribution due to the domain shift. In order to tackle this known issue, several transfer learning approaches have been proposed, where the knowledge of a trained model is transferred into another to improve performance with different data. However, most of these approaches require additional training steps, or they suffer from catastrophic forgetting that occurs when a trained model has overwritten previously learnt knowledge. We address both problems with a novel transfer learning approach that uses network aggregation. We train dataset-specific networks together with an aggregation network in a unified framework. The loss function includes two main components: a task-specific loss (such as cross-entropy) and an aggregation loss. The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator. We demonstrate that the proposed approach learns model aggregation at test time without any further training step, reducing the burden of transfer learning to a simple arithmetical operation. The proposed approach achieves comparable performance w.r.t. the baseline. Besides, if the aggregation operator has an inverse, we will show that our model also inherently allows for selective forgetting, i.e., the aggregated model can forget one of the datasets it was trained on, retaining information on the others.