• Multi-view contrastive learning overcomes data sparsity • Temporal regularisation stabilises user preferences across sequences • Popularity-aware defence mitigates promotion and camouflage attacks • Achieves stable performance within ± 1% even with 80% malicious clients • Maintain 90% of original user accuracy even when cold-start Federated sequential recommendation (FedSeqRec) allows many user devices to train a shared recommender without sending raw interaction histories to a central server, which is important for privacy. However, existing FedSeqRec methods still suffer from two key limitations: (1) most users have very short or sparse histories, especially when they only show interest in a few items for a short period, leaving the model with too little data to understand their preferences; and (2) in a sequential setting it is normal for interests to change suddenly, but the model may misinterpret these abrupt changes as anomalies. In this paper, we propose FORTRESS , a F ederated c O ntrastive R obus T RE commender for S equential S ystems, designed to address these limitations. To tackle the first issue, FORTRESS generates augmented versions of local interaction sequences on each client, so that the model can observe more plausible behaviour patterns and learn user preferences more reliably even when histories are short or sparse. This extra flexibility also gives adversaries more room to manipulate the training signal, so we complement it with a popularity-aware server-side regularizer that discourages rare or suspicious items from drifting into the same embedding clusters as genuinely popular items. To tackle the second issue, we introduce a temporal regularization term that discourages abrupt changes in user representations across adjacent subsequences, allowing the model to adapt to short-term interest shifts while still preserving stable long-term tastes. Experiments on three real-world datasets show that FORTRESS improves recommendation accuracy for sparse and cold-start users and substantially reduces the success of strong model poisoning attacks compared with competitive centralized and federated baselines.

Abstract The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to users. However, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users’ reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support (such as biomedical image diagnostics), is computed at the client level. We leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device’s computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenarios, our architecture demonstrates unparalleled performance and robustness in terms of both accuracy and efficiency.

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) multi-order dynamics, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) internal dynamics, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss.

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.