在推荐系统迈向多模态的今天，如何兼顾数据隐私与个性化图文理解？悉尼科技大学龙国栋教授团队联合香港理工大学杨强教授、张成奇教 授团队，提出全新框架 FedVLR。该工作解决了联邦环境下多模态融合的异质性难题，已被人工智能顶级会议 AAAI 2026 接收为 Oral Presentation。 在当今的推荐系统中，利用图像和文本等多模态信息来辅助决策已是标配。然而，当这一需求遭遇 联邦学习 —— 这一要求「数据不出本地」的隐私保护计算范式 时，情况变得极其复杂。 现有的联邦推荐往往面临两难：要么为了保护隐私而放弃繁重的多模态处理，仅使用 ID 特征；要么采用「一刀切」（One-size-fits-all）的粗暴融合策略，假设所 有用户对图文的偏好一致。 但现实是残酷的： 用户的「融合偏好」天生具有极大的异质性。 购买服装时，用户可能更依赖视觉冲击；而挑选数码产品时，详尽的参数文本可能才是关键。这 种偏好的差异，在数据不可见的联邦环境下，极难被捕捉。 为了打破这一瓶颈， 悉尼科技大学龙国栋教授团队，联合香港理工大学人工智能高等研究院杨强院长、香港理工大学深圳研究院张成奇院长推出了 FedVLR 框 架。 其核心洞 ...

Core Insights - The EMNLP 2025 conference will be held in Suzhou, China, from November 4-9, 2025, focusing on breakthroughs in data-driven NLP technologies and attracting thousands of researchers and engineers globally [1] - WeBank has had three papers accepted at EMNLP, focusing on federated large model frameworks, model compression, and knowledge transfer in inference [1] - WeBank is committed to becoming a leading AI-native bank, emphasizing technological self-reliance and innovation in AI, blockchain, big data, and cloud computing [3] Group 1: Research and Development - WeBank has published a total of nine papers in top conferences such as EMNLP, NeurIPS, CVPR, KDD, TIFS, and TKDE in 2025, marking a record high for the bank [3] - The bank's AI research focuses on key technologies such as federated model compression, large model distillation, and customized large model services [3][4] - WeBank has published over 50 papers in international conferences and journals, applying various technologies to core business areas like financial risk control, intelligent customer service, and fraud prevention [4] Group 2: Standards and Recognition - WeBank led the drafting of the national standard GB/T 46284-2025 for federated learning technology, which came into effect on October 5, 2025 [3] - The bank, in collaboration with Tongji University and Microsoft Research Asia, won the third prize in the CCF Technology Achievement Award for Natural Sciences in 2025, highlighting its technological innovation capabilities [3] Group 3: Future Directions - WeBank aims to deepen its AI core technology research, focusing on "financial technology" and "smart living" to convert research outcomes into industrial value [5] - The bank is dedicated to providing leading technological solutions for building a "safe, efficient, and fair" AI application ecosystem [5]

Core Insights - The EMNLP 2025 conference will be held in Suzhou, China, from November 4-9, 2025, focusing on breakthroughs in data-driven NLP technologies [1] - WeBank has three papers accepted at EMNLP, exploring federated large model frameworks, model compression, and knowledge transfer in reasoning [1] - WeBank is a pioneer in digital banking in China, committed to making financial services accessible to the public through technological innovation [2] Group 1 - WeBank is accelerating its transition from a "digital native" to an "AI native" bank, focusing on AI infrastructure, applications, and governance [2] - The bank has achieved a record high of nine papers accepted at top conferences in 2025, including EMNLP, NeurIPS, and CVPR [2] - WeBank led the development of the national standard GB/T 46284-2025 for federated learning technology, effective from October 5, 2025 [2] Group 2 - WeBank's AI research emphasizes a problem-oriented approach, aiming to address real industry pain points [3] - The bank has published over 50 papers in top international conferences, with technologies applied in financial risk control, intelligent customer service, and fraud prevention [3] - Future research will focus on transforming AI research outcomes into industrial value, contributing to a safe, efficient, and fair AI application ecosystem [3]

Core Insights - The G20 GPU financial AI ecosystem addresses challenges in traditional risk control systems, such as insufficient computing power and data fragmentation, by integrating hardware, software, and data collaboration for enhanced security in digital finance [1][2] - The ecosystem enables real-time risk identification, achieving risk assessment within 0.3 seconds for each transaction, significantly improving the efficiency and accuracy of fraud detection [1][2] Group 1: Risk Control System Enhancements - The G20 ecosystem allows for real-time sharing of risk characteristics among algorithm vendors, reducing the model update cycle from 1-2 weeks to 24 hours, resulting in a 22% increase in fraud interception rates and an 18% decrease in false positives [2] - The system incorporates 18 detection measures, including transaction behavior analysis and device security checks, to generate risk scores and determine transaction approval [1] Group 2: Data Sharing and Collaboration - The ecosystem has established a cross-institution risk data sharing mechanism with banks and insurance companies, utilizing federated learning to ensure data privacy while optimizing risk control models [2] - A participating bank reported a 15% improvement in credit card default risk prediction accuracy and a 0.8 percentage point reduction in non-performing loan rates after joining the data sharing initiative [2] Group 3: Ecosystem Impact - The G20 financial AI ecosystem has served over 20 financial institutions across various sectors, intercepting suspicious transactions worth over 1.5 billion and handling more than 300,000 risk events, thereby supporting the stable operation of digital finance [3]

Core Insights - The Chinese federated learning industry is experiencing steady growth driven by policy support, technological advancements, and market demand, with a projected market size of 254 million yuan in 2024, representing a year-on-year increase of 11.89% [1][8] - Federated learning effectively addresses the challenges of data silos and privacy security, enhancing model accuracy by over 20% in various applications such as financial risk control, medical joint diagnosis, and urban traffic optimization [1][8] Industry Overview - Federated learning (FL) is a distributed machine learning method aimed at enabling collaborative model training while protecting data privacy. It allows participants to train models locally using their own data and share encrypted model parameters with a central server, thus avoiding data sharing across institutions and complying with privacy regulations like GDPR [2] - The industry has evolved through four stages since the concept was introduced by Google in 2017: exploration, application, ecosystem building, and mature expansion [3] Market Size - The market size of the Chinese federated learning industry is expected to reach 254 million yuan in 2024, with a growth rate of 11.89% year-on-year [1][8] - The industry is supported by continuous iterations of technological frameworks, such as WeBank's FATE and Ant Group's shared intelligence platform, which incorporate privacy protection technologies like homomorphic encryption and secure multi-party computation [1][8] Key Companies - Leading companies in the federated learning sector include Ant Group and WeBank, with Ant Group holding a 36.7% market share in the privacy computing market for three consecutive years [8] - WeBank has pioneered the application of federated learning technology in the financial sector, with its open-source FATE framework becoming an industry benchmark [8] Industry Development Trends - The integration of federated learning with AI large models, edge computing, and 5G/6G technologies is expected to create a new paradigm of distributed AI collaboration [10] - Applications of federated learning are expanding beyond finance and healthcare into industrial internet, autonomous driving, and energy management, enhancing the technology's role in digital transformation [11][12] - The establishment of standards and the improvement of domestic policies are expected to strengthen the industry's foundation, with initiatives like the IEEE P3652.1 standard and the implementation of data security laws providing compliance support [13]

Core Viewpoint - The article discusses the FedPall algorithm, which addresses the feature drift problem in federated learning by combining prototype-based adversarial and collaborative learning techniques, achieving state-of-the-art performance across various datasets [2][10]. Methodology - The FedPall framework introduces an adversarial learning mechanism between clients and the server, enhancing feature representation alignment in a unified feature space through collaborative learning [3]. - A hierarchical integration strategy is developed to combine global prototypes with local features, facilitating client-server collaboration [5]. - The server trains a shared global amplifier and utilizes KL divergence to enhance heterogeneous information from different clients, mapping raw data to a unified feature space [5]. - The global classifier is distributed to each client, replacing the local classifiers to improve generalization and mitigate feature drift [6]. Performance Evaluation - FedPall was evaluated on three publicly available feature drift datasets: Digits, Office-10, and PACS, demonstrating superior accuracy compared to classical methods and state-of-the-art baselines [8][10]. - In the Office-10 dataset, FedPall achieved an overall accuracy approximately 3 percentage points higher than the second-best method, ADCOL [10]. - The Digits dataset results showed FedPall outperforming all other models, with an accuracy exceeding the second-best model, FedBN, by about 1.1 percentage points [10]. - FedPall consistently maintained higher average accuracy across all three datasets compared to ADCOL, with improvements ranging from 1.1 to 3 percentage points [12]. Future Directions - The research aims to validate the FedPall framework's generalization capabilities across other data modalities and task types in future studies [13].

Core Insights - Short video platforms have become a crucial battleground for brand promotion, with Douyin leveraging its large user base and precise algorithmic recommendations to attract businesses [1] - The "Giant Advertising" system launched by Douyin helps advertisers achieve efficient exposure and conversion through a bidding model, leading many companies to outsource their advertising accounts to professional operation companies [1] Group 1: Industry Challenges and Needs - Many small and medium-sized enterprises face multiple challenges when managing Douyin ads, including budget wastage due to lack of experience with billing models like OCPM and CPC [2] - Creative content production is often weak, requiring cross-disciplinary collaboration that non-professional teams struggle to achieve [2] - Weak data analysis capabilities hinder effective interpretation of vast data metrics, making it difficult to adjust advertising strategies [2] Group 2: Core Competencies of Operation Companies - Professional operation companies excel in building precise user profiles by deeply analyzing target customer segments and utilizing Douyin's tagging system [5] - They employ a data-driven dynamic adjustment mechanism, using intelligent bidding systems combined with manual intervention to optimize key performance indicators [5] - Established teams have standardized content production processes, enhancing the replicability of successful content, with professionally produced ads retaining viewer attention 60% longer than self-made content [6] Group 3: Industry Issues and Partner Selection - The surge in market demand has led to some negative phenomena, such as studios charging high fees while using deceptive methods to inflate performance metrics [7] - Companies should verify the authenticity of case studies and request recent advertising data reports to avoid misleading claims [9] - It is essential to confirm the certification status of operation partners and ensure transparency in contract terms to prevent disputes [9] Group 4: Future Trends - The operation industry is evolving towards automation and precision, with automated tools taking over basic tasks, allowing human resources to focus on strategic decision-making [10] - The ability to integrate across multiple platforms will become a new competitive barrier, with service providers managing resources across Douyin, Kuaishou, and WeChat ecosystems being more favored [10] - Breakthroughs in privacy computing may lead to new models for collaborative advertising targeting while protecting user privacy [10]

Core Value - Eli Lilly's Lilly TuneLab is a machine learning platform that leverages over $1 billion worth of drug development models accumulated over years, making it one of the most valuable datasets in the industry [4] - The platform utilizes Federated Learning technology, allowing biotech companies to use Lilly's AI models for drug discovery without sharing their proprietary data [4] Collaboration Model - Biotech companies using the platform are required to contribute their training data, which is aimed at improving the platform and benefiting the entire ecosystem and patients [5] - The initiative is designed to empower smaller biotech firms with the advanced AI capabilities typically available to larger companies [5] Addressing Pain Points - The platform addresses a fundamental barrier faced by small biotech companies, which is the lack of large-scale, high-quality data necessary for training effective models [7] - Lilly TuneLab compresses decades of learning into an immediately usable intelligence resource, thus alleviating the data scarcity issue [7] Future Plans - Eli Lilly plans to enhance TuneLab with additional features, such as in vivo small molecule prediction models, to continuously expand its capabilities [8] - The launch of the AI drug development platform represents a proactive attempt by a pharmaceutical giant to reshape the industry ecosystem and accelerate innovation for data-deficient biotech companies [8]

Core Insights - The report highlights the transition of AI in drug development from concept to reality, with significant advancements expected in 2024, marked by the Nobel Prize awarded for AlphaFold2, indicating a new era in AI-driven pharmaceuticals [1][4][13] - Multi-omics AI applications are projected to achieve a 1000-fold reduction in costs and efficiency in the pharmaceutical sector, with the first AI-driven blockbuster drug nearing approval [1][4][16] - The industry is witnessing a paradigm shift as major tech companies and pharmaceutical giants invest heavily in AI, with over $50 billion in AI drug development-related transactions in the past five years [1][5][6] Group 1: Industry Dynamics - AI drug development is moving towards practical applications, with significant breakthroughs in model transparency and regulatory frameworks, such as the EU's AI Act promoting explainability [1][4][31] - Key elements driving the industry include computational power, data integration, and advanced modeling techniques, with major cloud providers like Amazon, Google, and Microsoft offering robust resources [1][4][36] - The emergence of federated learning technologies is breaking down data silos, enabling cross-industry collaborations to enhance drug discovery [1][4][36] Group 2: Major Players and Investments - Tech giants like NVIDIA and Google are actively entering the AI pharmaceutical space, with NVIDIA investing in 13 AI drug companies and Google restructuring its AI divisions for clinical trials [1][5][6] - Leading pharmaceutical companies, including Merck and Pfizer, are committing hundreds of millions to AI-related initiatives, reflecting a strategic shift towards AI in drug development [1][5][6] - The report emphasizes the importance of companies with rich pipelines and proven capabilities in AI drug development, suggesting a focus on firms like Insilico Medicine and CrystalGenomics [1][6][19] Group 3: Future Outlook - The report anticipates that AI will revolutionize drug development, diagnostics, and treatment methodologies, with significant economic returns expected from AI-enabled innovations [1][19][20] - By 2030, the entire pharmaceutical industry is projected to experience exponential growth driven by AI, with substantial improvements in efficiency and cost-effectiveness [1][19][20] - The integration of AI in drug development is expected to enhance the speed and accuracy of clinical trials, ultimately leading to faster market entry for new therapies [1][39]

Core Viewpoint - Data has become a "gold mine" and a hotspot for investment in the smart connected vehicle sector, with recent government signals promoting and regulating cross-border data flow, which is expected to benefit the import and export of smart vehicles [3][5]. Group 1: Data Generation and Importance - Smart connected vehicles generate massive amounts of data daily, reaching terabytes (TB), including various types of information such as facial expressions, actions, voice data, and vehicle location [4]. - The increasing import of smart vehicles like Tesla and the growing export of Chinese smart vehicles highlight the need for effective cross-border data flow management [5]. Group 2: Regulatory Framework - China has established a policy framework for cross-border data flow, including the implementation of the Data Security Law and the Personal Information Protection Law, which provide a legal basis for data management in the smart vehicle sector [5][6]. - The upcoming regulations, such as the "Automotive Data Export Safety Guidelines (2025 Edition)" and the "Regulations on Promoting and Regulating Cross-Border Data Flow," indicate a move towards more specialized and detailed data governance [6][12]. Group 3: Global Data Governance Challenges - Different countries have varying data governance models, with the EU's GDPR imposing strict data localization requirements, presenting challenges for Chinese smart vehicle companies operating in the EU market [7]. - The need for compliance with international regulations is pushing foreign brands in China to adapt their data management strategies, as seen with Tesla's establishment of a local data center [9]. Group 4: Technological Innovations and Compliance - Technological innovations such as privacy computing and federated learning are becoming key drivers for improving compliance efficiency in cross-border data flow [10]. - Emerging technologies like dynamic de-identification and intelligent encryption are expected to become standard practices for ensuring data security during cross-border transmission [11]. Group 5: Industry Self-Regulation and Future Outlook - Industry self-regulation is crucial for enhancing compliance levels in cross-border data flow, with proposed management systems focusing on pre-assessment, real-time monitoring, and post-audit processes [11]. - The promotion and regulation of cross-border data flow are seen as guiding principles for healthy industry development, encouraging companies to integrate compliance capabilities into their export strategies [12].