Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation

Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. However, the conventional FL method can introduce security risks when dealing with diverse client data, potentially compromising privacy and data integrity. To address these challenges, we present a differential privacy (DP) federated deep learning framework in medical image segmentation. In this paper, we extend our similarity weight aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private similarity-weighted aggregation algorithm for brain tumor segmentation in multi-modal magnetic resonance imaging (MRI). Our DP-SimAgg method not only enhances model segmentation capabilities but also provides an additional layer of privacy preservation. Extensive benchmarking and evaluation of our framework, with computational performance as a key consideration, demonstrate that DP-SimAgg enables accurate and robust brain tumor segmentation while minimizing communication costs during model training. This advancement is crucial for preserving the privacy of medical image data and safeguarding sensitive information. In conclusion, adding a differential privacy layer in the global weight aggregation phase of the federated brain tumor segmentation provides a promising solution to privacy concerns without compromising segmentation model efficacy. By leveraging DP, we ensure the protection of client data against adversarial attacks and malicious participants

Comparative constructions of similarity in Northern Samoyedic languages

The purpose of this paper is to analyze the suffixes which are used in Northern Samoyedic languages to build comparative constructions of equality. Depending on the language, the suffixes may perform three functions: word-building, form-building, and inflectional. When they mark the noun, they serve as simulative suffixes and are employed to build object comparison. In the inflectional function, these suffixes mark the verb and are a means of constructing situational comparison. In this case, they signal the formation of a special mood termed the Approximative. This paper provides a detailed description of the Approximative from paradigmatic and syntagmatic perspectives

Monitoring of batch industrial crystallization with growth, nucleation, and agglomeration, part 2 : Structure design for state estimation with secondary measurements

This work investigates the design of alternative monitoring tools based on state estimators for industrial crystallization systems with nucleation, growth, and agglomeration kinetics. The estimation problem is regarded as a structure design problem where the estimation model and the set of innovated states have to be chosen; the estimator is driven by the available measurements of secondary variables. On the basis of Robust Exponential estimability arguments, it is found that the concentration is distinguishable with temperature and solid fraction measurements while the crystal size distribution (CSD) is not. Accordingly, a state estimator structure is selected such that (i) the concentration (and other distinguishable states) are innovated by means of the secondary measurements processed with the geometric estimator (GE), and (ii) the CSD is estimated by means of a rigorous model in open loop mode. The proposed estimator has been tested through simulations showing good performance in the case of mismatch in the initial conditions, parametric plant-model mismatch, and noisy measurements

Effect of polydextrose on subjective feelings of appetite during the satiation and satiety periods: a systematic review and meta-analysis

Subjective feelings of appetite are measured using visual analogue scales (VAS) in controlled trials. However, the methods used to analyze VAS during the Satiation (pre- to post-meal) and Satiety (post-meal to subsequent meal) periods vary broadly, making it difficult to compare results amongst independent studies testing the same product. This review proposes a methodology to analyze VAS during both the Satiation and Satiety periods, allowing us to compare results in a meta-analysis. Methods: A methodology to express VAS results as incremental areas under the curve (iAUC) for both the Satiation and Satiety periods is proposed using polydextrose as a case study. Further, a systematic review and meta-analysis on subjective feelings of appetite was conducted following the PRISMA methodology. Meta-analyses were expressed as Standardized Mean Difference (SMD). Results: Seven studies were included in the meta-analysis. There were important differences in the methods used to analyze appetite ratings amongst these studies. The separate subjective feelings of appetite reported were Hunger, Satisfaction, Fullness, Prospective Food Consumption, and the Desire to Eat. The method proposed here allowed the results of the different studies to be homogenized. The meta-analysis showed that Desire to Eat during the Satiation period favors polydextrose for the reduction of this subjective feeling of appetite (SMD = 0.24, I2 < 0.01, p = 0.018); this effect was also significant in the sub-analysis by sex for the male population (SMD = 0.35, I2 < 0.01, p = 0.015). There were no other significant results. Conclusion: It is possible to compare VAS results from separate studies. The assessment of iAUC for both the Satiation and Satiety periods generates results of homogeneous magnitudes. This case study demonstrates, for the first time, that polydextrose reduces the Desire to Eat during the Satiation period. This may explain, at least in part, the observed effects of polydextrose on the reduction of levels of energy intake at subsequent meals

Effect of polydextrose on subjective feelings of appetite during the satiation and satiety periods: a systematic review and meta-analysis

Subjective feelings of appetite are measured using visual analogue scales (VAS) in controlled trials. However, the methods used to analyze VAS during the Satiation (pre- to post-meal) and Satiety (post-meal to subsequent meal) periods vary broadly, making it difficult to compare results amongst independent studies testing the same product. This review proposes a methodology to analyze VAS during both the Satiation and Satiety periods, allowing us to compare results in a meta-analysis. Methods: A methodology to express VAS results as incremental areas under the curve (iAUC) for both the Satiation and Satiety periods is proposed using polydextrose as a case study. Further, a systematic review and meta-analysis on subjective feelings of appetite was conducted following the PRISMA methodology. Meta-analyses were expressed as Standardized Mean Difference (SMD). Results: Seven studies were included in the meta-analysis. There were important differences in the methods used to analyze appetite ratings amongst these studies. The separate subjective feelings of appetite reported were Hunger, Satisfaction, Fullness, Prospective Food Consumption, and the Desire to Eat. The method proposed here allowed the results of the different studies to be homogenized. The meta-analysis showed that Desire to Eat during the Satiation period favors polydextrose for the reduction of this subjective feeling of appetite (SMD = 0.24, I2 < 0.01, p = 0.018); this effect was also significant in the sub-analysis by sex for the male population (SMD = 0.35, I2 < 0.01, p = 0.015). There were no other significant results. Conclusion: It is possible to compare VAS results from separate studies. The assessment of iAUC for both the Satiation and Satiety periods generates results of homogeneous magnitudes. This case study demonstrates, for the first time, that polydextrose reduces the Desire to Eat during the Satiation period. This may explain, at least in part, the observed effects of polydextrose on the reduction of levels of energy intake at subsequent meals

Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation

We introduce similarity weighted aggregation, a principled and efficient method for regularized weight aggregation in federated learning. Our method is adapted to non-IID collaborators and is simultaneously cost-efficient. This is the first method to propose a slidingwindow to select the collaborators, to the best of our knowledge. We demonstrate our method on the federate training task of the FeTS 2021 challenge.We proposed two variations coined SimilarityWeighted Aggregation (SimAgg) and Regularized Aggregation (RegAgg). SimAgg results on internal validation data demonstrate that the proposed method outperforms the baseline FedAvg. The method SimAgg by our team HTTUAS won 2nd position on both leaderboards in FeTS2021 challenge. SimAgg is the only method to be among the top performing methods on both the leaderboards, making it robust and reliable to data variations. Our solution is open sourced at: https://github.com/dskhanirfan/ FeTS202