Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of $0.94$ on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets $\epsilon \in \{1,3,6,10\}$. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of $0.94$ for $\epsilon =6$. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of $0.76$ in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.

The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.

Background: Limited data availability impedes the advancements of artificial intelligence (AI) applications in surgery to date. Federated learning (FL), a novel privacy-focused machine learning technique, introduces a decentralized framework to facilitate multicenter modeling. This study constructs a FL network, including differential privacy (DP), to predict 90-day mortality following colorectal surgery. Methods: Patients undergoing elective colorectal surgery across three tertiary centers (C1–C3) between January 2015 and December 2021 were retrospectively enrolled. Neural networks (NN) for mortality prediction were trained and validated for all three centers individually and after data aggregation using centralized and distributed FL analysis. Local and central DP was then applied as additional data protection framework components. Areas under the receiver operating characteristic (AUROC) and precision-recallcurves (AUPRC) including 95%-confidence intervals were calculated. Results: A total of 2959 patients (mean [SD] age: 56.9 [16.8] years; n = 1677 [56.7%] male) were enrolled. The 90-day mortality rate was 3.1% (n = 92). Local NNs achieved AUROCs of 0.80 ([0.74–0.87]; C1), 0.81 ([0.75–0.87]; C2), and 0.84 ([0.75–0.92]; C3) and corresponding AUPRCs reaching 0.26 [0.11–0.41], 0.31 [0.21–0.41], and 0.21 [0.11–0.32], respectively. An aggregated centralized NN (NN _CZ ) achieved an AUROC of 0.81 [0.76–0.85] and AUPRC of 0.29 [0.22–0.36]. Distributed FL (NN _FL ) was comparable (AUROC: 0.78 [0.72–0.84], P = 0.67; AUPRC: 0.26 [0.17–0.35], P = 0.44) to the centralized model. Central DP reduced the performance of FL-based prediction by 5% in AUROC (0.74 [0.64–0.84]) and 35% in AUPRC (0.17 [0.10–0.23]). Local DP almost diminished the performance (AUROC: 0.52 [0.49–0.55]; AUPRC: 0.05 [0.03–0.06]). Feature importance analysis revealed age, blood status and the Charlson Comorbidity Index as highest weighted features for both NN _CZ and NN _FL . Conclusion: Federated learning demonstrates similar performance to centralized machine learning in preoperative mortality prediction, providing an encouraging framework to accelerate the development of future surgical AI applications. Improving data privacy through DP is associated with compromises in model performance.

Given the Internet of Things rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance. Using machine learning techniques has been shown to improve detecting anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy. To better address these shortcomings, federated learning is being introduced. It enables devices to collaboratively train and evaluate a shared model while keeping personal data on-site (e.g., smart homes, intensive care units, hospitals, etc.), thus minimizing the possibility of an attack and fostering real-time distribution of models and learning. The paper investigates the performance of federated learning in comparison to deep learning, with respect to network intrusion detection in ambient assisted living environments. The results demonstrate comparable performances of federated learning with deep learning, while achieving improved data privacy and security.

Background: Machine Learning (ML) is increasingly being adopted in biomedical research, however, its potential for outcome prediction in visceral surgery remains uncertain. This study compares the potential of ML methods for preoperative 90-day mortality (90DM) prediction of an aggregated multi-organ approach to conventional scoring systems and individual organ models. Methods: This retrospective cohort study enrolled patients undergoing major elective visceral surgery between 2014 and 2022 across two tertiary centers. Multiple ML models for preoperative 90DM prediction were trained, externally validated and benchmarked against the American Society of Anesthesiologists (ASA) score and revised Charlson Comorbidity Index (rCCI). Areas under the receiver operating characteristic (AUROC) and precision recall curves (AUPRC) including standard deviations were calculated. Additionally, individual models for esophageal, gastric, intestinal, liver, and pancreatic surgery were developed and compared to an aggregated approach. Results: 7711 cases encompassing 78 features were included. Overall 90DM was 4% (n = 309). An XBoost classifier demonstrated the best performance and high robustness following external validation (AUROC: 0.86 [0.01]; AUPRC: 0.2 [0.04]). All models outperformed the ASA score (AUROC: 0.72; AUPRC: 0.08) and rCCI (AUROC: 0.81; AUPRC: 0.11). rCCI, patient age and C-reactive protein emerged as most decisive model weights. Models for gastric (AUROC: 0.88 [0.13]; AUPRC: 0.24 [0.26]) and intestinal surgery (AUROC: 0.87 [0.05]; AUPRC: 0.17 [0.09]) revealed the highest organ-specific performances, while pancreatic surgery yielded the lowest results (AUROC: 0.66 [0.08]; AUPRC: 0.22 [0.12]). A combined multi-organ approach (AUROC: 0.84 [0.04]; AUPRC: 0.21 [0.06]) demonstrated superiority over the weighted average across all organ-specific models (AUROC: 0.82 [0.07]; AUPRC: 0.2 [0.13]). Conclusion: ML offers robust preoperative risk stratification for 90DM in elective visceral surgery. Leveraging training across multi-organ cohorts may improve accuracy and robustness compared to organ-specific models. Prospective studies are needed to confirm the potential of ML in surgical outcome prediction.