Artificial Intelligence (AI) technologies have revolutionized numerous fields, yet their applications often rely on costly and time-consuming data collection processes. Federated Learning (FL) offers a promising alternative by enabling AI models to be trained on decentralized data where data is scattered across clients (distributed nodes). However, existing FL approaches struggle to match the performance of centralized training due to challenges such as heterogeneous data distribution and communication delays, limiting their potential for breakthroughs. W e observe that many real-world use cases involve hybrid data regimes, in which a server (center node) has access to some data while a large amount of data is distributed across associated clients. T o improve the utilization of decentralized data under this regime, address data heterogeneity issue, and facilitate asynchronous communication between the server and clients, we propose a dual learning approach that leverages centralized data at the server to guide the merging of model updates from clients. Our method accommodates scenarios where server data is out-of-domain relative to decentralized client data, making it applicable to a wide range of use cases. W e provide theoretical analysis demonstrating the faster convergence of our method compared to existing methods. Furthermore, experimental results across various scenarios show that our approach significantly outperforms existing technologies, highlighting its potential to unlock the value of large amounts of decentralized data.

Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored. In this work, we present an in-depth empirical analysis and demonstrate that, contrary to previous observations, scaling the decoder yields significant performance gains, always exceeding those achieved by encoder scaling alone. We also identify label noise as a key challenge in STR, particularly in real-world data, which can limit the effectiveness of STR models. To address this, we propose Cloze Self-Distillation (CSD), a method that mitigates label noise by distilling a student model from context-aware soft predictions and pseudolabels generated by a teacher model. Additionally, we enhance the decoder architecture by introducing differential cross-attention for STR. Our methodology achieves state-of-the-art performance on 10 out of 11 benchmarks using only real data, while significantly reducing the parameter size and computational costs.

Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe responses, remains underexplored in the context of FedLLM. In FedLLM, client data used for training may contain harmful content, leading to unsafe LLMs that generate harmful responses. Aggregating such unsafe LLMs into the global model and distributing them to clients may result in the widespread deployment of unsafe LLMs. To address this issue, we incorporate two well-known RAI methods into FedLLM: the safety filter and constitutional AI. Our experiments demonstrate that these methods significantly enhance the safety of the LLM, achieving over a 20% improvement on AdvBench, a benchmark for evaluating safety performance.