In real-world scenarios, multimodal information continuously evolves, with new entity and relation types emerging, necessitating timely updates to multimodal knowledge graphs for supporting downstream tasks.However, existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios.To this end, this paper proposes the Continual Multimodal Entity and Relation Joint Extraction (CMERJE) task and a Multimodal Prompt-based Boundaryenhanced Continual (MPBoCo) framework.Specifically, MPBoCo incrementally stores task-specific knowledge via learnable multimodal prompts, dynamically matches relevant prompts for each instance, and fuses them into a frozen backbone model for task-specific reasoning.Subsequently, the boundary-enhanced dual-branch module leverages the auxiliary branch to preserve local syntactic continuity and provide boundary guidance.Experimental results demonstrate that MPBoCo achieves superior performance in real-world scenarios, significantly outperforming baseline methods by 5.5% and 7.2% in 10-task and 5-task settings, respectively.

Federated learning is an emerging machine learning paradigm that effectively alleviates the data silo problem by distributing the model training process to multiple data holders. However, data from real-world mobile applications often has multi-label and presents a long-tailed distribution, where labels are generally non-independent and non-identically distributed, thereby increasing the challenges caused by data heterogeneity. To address the above problems, we propose a Federated Chain Context Optimization (FedCCO) for long-tailed multi-label image classification. Inspired by the success of Chain of Though (CoT) in enhancing the semantic expressive ability of models, this method fine-tunes the CLIP model using semantic descriptive vectors generated by the Chain Context Optimization (ChCoOp) to establish semantic correlations between head and tail classes across clients, which improves the ability of the model to recognize tail classes. The experimental results show that the FedCCO achieves satisfactory performance in long-tailed multi-label image classification in federated learning on VOC-LT and COCO-LT datasets.

Cross-device federated learning (FL) enables collaborative model training across heterogeneous edge devices while preserving data privacy. However, system heterogeneity remains a major challenge, especially under constrained computation and memory resources. Although model compression—particularly knowledge distillation—has been widely used to reduce overhead, it inevitably introduces model heterogeneity, leading to degraded performance. To address this overlooked issue, we propose AHFL, a resource-adaptive and data-heterogeneity-aware federated learning framework. AHFL employs three coordinated strategies: a data-driven client grouping mechanism to assess and exploit heterogeneity levels, adaptive model compression tailored to each group’s resource profile, and a novel group distribution representation module with theoretical convergence guarantees to mitigate performance degradation caused by model heterogeneity. Extensive experiments demonstrate that AHFL reduces computational cost by 1.7× while simultaneously improving global accuracy by 4.13%. In experiments using the same compressed architecture, AHFL narrows the global-local accuracy gap to under 2%, achieving accuracy improvements of +7.47% (local) and +3.29% (global). The code is available at: https://github.com/CST-FederatedLearning/AHFL.

Federatedlearning has garnered significant attention in the Internet of Things and healthcare applications due to its ability to train a shared global model across distributed clients. However, imbalanced data distribution leads to model discrepancies among clients. Most existing methods adopt implicit alignment strategies while overlooking explicit modeling of geometric and directional discrepancies in feature representations, which undermines local model optimization. To address this issue, we propose a method of representation deviation alignment in federated learning, which projects features onto the principal feature space to measure deviations between local and global feature representations explicitly. Specifically, Federated learning with Representation Deviation Alignment (FedRDA) employs a feature encoder to extract compact features and construct unbiased principal feature spaces for global and local models. Then, the residual projection in the feature space serves as a quantitative measure of the representation deviation, effectively capturing the latent direction differences between models. Besides, we introduce a representation consistency alignment strategy, which ensures that the distribution of local client features becomes more uniform within the global feature space. Extensive experiments on SVHN, CIFAR-10, CIFAR-100, Tiny-ImageNet, and GC10 demonstrate that FedRDA effectively reduces the classifier bias caused by representational differences.

Federated semi-supervised learning ( FSSL ) involves training a model in a federated environment using a few labeled samples and many unlabeled samples . Compared with semi-supervised learning, FSSL faces more complex data situations, especially when data are non-independently and identically distributed ( non-IID ), adding more challenges to the learning process. The previous method addresses the aforementioned issues by enlarging the training sample space through multiple client random sampling and reweighting the parameters. Although it achieves high accuracy, it sacrifices communication efficiency. In this study, we propose PDCFed, a two-stage sampling method that uses the P redicted D istribution C hanges of samples after different data augmentations . We evaluate the credibility of the samples based on the maximum probability predicted by weak augmentation. When the samples are in a less reliable space, they are further sampled after adjusting the predicted distribution changes using a Gaussian function . To enhance the model’s generalization ability , an entropy penalty term is incorporated after unsupervised training loss. Extensive experiments demonstrate that this method outperforms existing methods on three datasets with non-IID data and significantly improves communication efficiency.

Memory race recording has been proved to be a hard problem in multithreaded deterministic record-replay. It is important to develop an efficient memory race recording algorithm. However, most of the prior work tries to record all memory conflicts, whether they affect deterministic replay or not, resulting a relatively large memory race log. This paper proposes an innovative synchronization aware point-to-point memory race recorder, called SAMR. SAMR analyzes memory conflicts introduced by synchronization operations and classifies them into harmful synchronization conflicts and harmless synchronization conflicts. Harmless synchronization conflicts are filtered out by identifying synchronization operations when recording, and a reduced memory race log is achieved. At the same time, SAMR reduces hardware overhead by using signatures instead of cache memory. Simulations with splash-2 workloads on 8-core CMP system show that SAMR can achieve small memory race size (~2 bytes per thousand memory instructions), good scalability in log size and low bandwidth overhead (<; 5%), while not needing too much hardware state (~1129 bytes).

Shared-memory multithreaded programs running on chip multiprocessors (CMPs) tend to be nondeterministic. Two-phase deterministic record-replay is an effective approach to solve this nondeterminism. This paper proposes an efficient deterministic record-replay named Fly Replay. During recording, Fly Replay logs not only the right dependencies of memory races but also the pseudo dependencies constituted by predecessors of memory races into per-thread log. During replay, Fly Replay produces wakeup messages actively to trigger successors in time, achieving low communication overhead and fast replay speed. At the same time, Fly Replay reduces hardware overhead by using hardware signatures. Simulation shows that Fly Replay reduces the log size for splash2 workloads by 40% on average compared with RTR and Rerun in 4-core systems, and has good scalability in log size. More importantly, Fly Repaly can achieve replay speed within 1%~18% of the native execution speed without record-replay.