Bruno Casella

@inproceedings{24:casella:sgx,

title = {A Performance Model for Confidential Federated Learning},

author = {Bruno Casella and Iacopo Colonnelli and Gianluca Mittone and Robert Birke and Walter Riviera and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},

url = {https://iris.unito.it/retrieve/b5877a97-2d8d-4e95-8791-0aa4a1b953b3/DLSP___CONFIDENTIAL_FL.pdf},

year  = {2024},

date = {2024-05-01},

booktitle = {Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024},

address = {San Francisco, CA},

abstract = {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.},

keywords = {confidential, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:casella:onchain,

title = {Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting},

author = {Bruno Casella and Lorenzo Paletto},

url = {https://iris.unito.it/bitstream/2318/1902652/1/6.%20ICBC23%20-%20PREDICTING%20BTC.pdf},

doi = {https://doi.org/10.1109/ICBC56567.2023.10174989},

year  = {2023},

date = {2023-01-01},

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

abstract = {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.},

note = {https://ieeexplore.ieee.org/document/10174989},

keywords = {epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:casella:ERGANs,

title = {Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning},

author = {Matteo Pennisi and Federica Proietto Salanitri and Giovanni Bellitto and Bruno Casella and Marco Aldinucci and Simone Palazzo and Concetto Spampinato},

url = {https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Pennisi_Experience_Replay_as_an_Effective_Strategy_for_Optimizing_Decentralized_Federated_ICCVW_2023_paper.pdf},

doi = {10.1109/ICCVW60793.2023.00362},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023},

abstract = {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.},

note = {https://ieeexplore.ieee.org/document/10350429},

keywords = {ai, confidential},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{casella2022benchmarking,

title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks},

author = {Bruno Casella and Roberto Esposito and Carlo Cavazzoni and Marco Aldinucci},

editor = {Marco Anisetti and Angela Bonifati and Nicola Bena and Claudio Ardagna and Donato Malerba},

url = {https://ceur-ws.org/Vol-3340/paper40.pdf},

year  = {2022},

date = {2022-01-01},

booktitle = {Proceedings of the 1st Italian Conference on Big Data and Data Science, ITADATA 2022, September 20-21, 2022},

volume = {3340},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {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.},

keywords = {confidential, eupilot},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{22:VISAPP:transferlearning,

title = {Transfer Learning via Test-time Neural Networks Aggregation},

author = {Bruno Casella and Alessio Chisari and Sebastiano Battiato and Mario Giuffrida.},

editor = {Giovanni Maria Farinella and Petia Radeva and Kadi Bouatouch},

url = {https://iris.unito.it/retrieve/handle/2318/1844159/947123/TRANSFER_LEARNING_VIA_TEST_TIME_NEURAL_NETWORKS_AGGREGATION.pdf},

doi = {10.5220/0010907900003124},

isbn = {978-989-758-555-5},

year  = {2022},

date = {2022-01-01},

booktitle = {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},

pages = {642–649},

publisher = {SciTePress},

organization = {INSTICC},

abstract = {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.},

keywords = {confidential},

pubstate = {published},

tppubtype = {inproceedings}

}