The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

This paper tackles the critical issue of privacy in Natural Language Processing (NLP) systems that process sensitive data by introducing a novel framework combining differential privacy and adversarial training. The proposed solution ensures formal privacy guarantees by minimizing the influence of individual data points on the model’s behavior, effectively preventing information leakage. Simultaneously, adversarial training is applied to strengthen model robustness against privacy attacks by exposing it to adversarial examples during training. The framework is rigorously evaluated across various NLP tasks, demonstrating its capability to balance privacy preservation with high utility effectively. These results mark a significant advancement in developing secure and reliable NLP systems, particularly for applications requiring stringent data confidentiality, such as healthcare and finance.

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities.

Personalized learning pathways are difficult to support in distributed educational systems because learner records cannot always be centralized and one shared federated predictor may not represent heterogeneity across institutions, learning tasks, and individual learners. We propose FedPath-MTL, a federated multi-task framework with global, task-specific, and learner-specific parameters, a bounded future-error proxy, a dynamic task graph, reliability-aware aggregation, and a constrained primal–dual beam-search planner. We evaluate only the next-outcome predictive component on four public educational releases: ASSISTments 2009–2010 Combined, Junyi Academy 2018–2019, EdNet-KT1, and OULAD. Every method is evaluated both before and after the same validation-only temperature-scaling protocol, and AUC, negative log-likelihood (NLL), Brier score, and 15-bin expected calibration error (ECE) are reported. The pooled central comparator has the highest mean AUC on all four releases. FedPath-MTL AUC ranges from 0.531 to 0.580, and identically calibrated ECE ranges from 0.098 to 0.127; matched comparisons with FedAvg-style training do not show a uniform AUC or calibration advantage. Observed school identifiers define clients only for ASSISTments; the other releases use explicitly labeled simulation partitions. The public logs do not jointly identify actions, propensities, subsequent-learning rewards, resource catalogs, and institutional constraints, so no pathway-effectiveness or causal learning-gain claim is made. Software tests verify equation-level implementation of the planner but do not validate educational benefit or real-institution constraint satisfaction.

Visual Internet-of-Things (IoT) cameras and institution-controlled edge gateways increasingly collect artwork images in museums, galleries, and heritage sites. Centralizing these images can expose collection contents, exhibition layouts, and contextual information. This paper proposes FedArtSense, a privacy-preserving federated learning framework for artistic style and medium classification. FedArtSense combines discrepancy-adaptive dual-space prototype alignment, client-level differential privacy for model and prototype releases, and importance-aware shared sparsification compatible with secure aggregation. Experiments on WikiArt, ArtBench-10, and a seven-class Behance Artistic Media subset use emulated non-IID client partitions, persistent acquisition shifts, constrained uplinks, and client dropout. Under the default client-level target $(ϵ,\delta )=(6,{10}^{-5})$, FedArtSense obtains accuracies of $64.2\%$, $81.2\%$, and $75.3\%$, respectively, while reducing cumulative WikiArt uplink traffic to $11.5$ GiB. The results support FedArtSense as a privacy–utility–communication trade-off for gateway-assisted artistic image classification; retrieval, detection, aesthetic prediction, and direct battery-powered camera training are outside the evaluated scope.

Federated learning enables privacy-preserving training of spiking neural networks on distributed neuromorphic and event-based data, but it also exposes the global model to stealthy backdoor attacks injected by malicious clients. Compared with conventional artificial neural networks, federated spiking neural networks are more difficult to sanitize because malicious behavior may be encoded not only in spatial filters but also in spike timing, membrane dynamics, and temporal firing sparsity. Existing backdoor defenses usually require repeated federated retraining, access to client data, trigger synthesis, or computationally expensive model repair, which limits their practicality in low-power neuromorphic deployment. This paper proposes FedTSR, a fast post-training backdoor removal framework for spiking neural networks trained through federated learning. FedTSR introduces two coordinated algorithms: temporal synaptic risk estimation, which identifies backdoor-sensitive synaptic groups by measuring abnormal spike-response contributions across simulation timesteps using a small clean calibration set, and spike-consistency recalibration, which restores benign task performance through lightweight membrane-potential alignment and firing-rate regularization without restarting federated training. The proposed method is trigger-agnostic, client-independent, and compatible with surrogate-gradient trained spiking models. Here, trigger-agnostic means that FedTSR does not use trigger identity during repair; it should not be interpreted as certified robustness against every adaptive trigger family. Experiments on real neuromorphic and vision benchmarks, including N-MNIST, CIFAR10-DVS, DVS Gesture, and CIFAR-10, show that FedTSR substantially reduces attack success rate while preserving clean accuracy under multiple backdoor patterns, poisoning ratios, and non-IID federated settings. The results indicate that exploiting temporal spike dynamics provides an efficient and effective route for sanitizing compromised federated spiking neural networks.