Demo Data from SIGIR 2026 / reviewers in the wild / expert
Peishen Yan
dblp:365/5943
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2026
0000-0002-2433-2128ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Computer networks · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
3 papers |
Security and privacy of machine learning · 94% Blockchain and cryptocurrency security · 6% | |
| Artificial intelligence
2 papers |
Efficient and distributed learning · 75% Trustworthy machine learning · 25% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
federated learning |
1.5 | 2 | 2024 | OFL-W3: A One-shot Federated Learning System on Web 3.0 · Proc. VLDB Endow. 2024 SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks · ECCV (19) 2024 |
Security and privacy of machine learning › adversarial attack
backdoor attack |
0.9 | 1 | 2025 | Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment · ICCV 2025 |
Security and privacy of machine learning › adversarial attack › backdoor attack
federated learning backdoor |
0.9 | 1 | 2025 | Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment · ICCV 2025 |
Machine learning › Trustworthy machine learning › robustness › adversarial robustness
attack-agnostic robustness |
0.8 | 1 | 2024 | SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks · ECCV (19) 2024 |
Machine learning › Efficient and distributed learning › federated learning › communication-efficient federated learning
one-shot federated learning |
0.8 | 1 | 2024 | OFL-W3: A One-shot Federated Learning System on Web 3.0 · Proc. VLDB Endow. 2024 |
Security and privacy of machine learning
federated learning security |
0.8 | 1 | 2024 | SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks · ECCV (19) 2024 |
Security and privacy of machine learning › federated learning security
robust federated learning |
0.8 | 1 | 2024 | SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks · ECCV (19) 2024 |
Blockchain and cryptocurrency security
smart contract |
0.2 | 1 | 2024 | OFL-W3: A One-shot Federated Learning System on Web 3.0 · Proc. VLDB Endow. 2024 |
Methods — techniques the papers use, named apart from their topics
incentive mechanism · 2.3learnable masks · 1.5fine-grained masking · 1.5federated learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | FedCod: An Efficient Coded Communication Protocol for Cross-Silo Federated Learning
Peishen Yan, Jun Li 0004, Hao Wang 0022, Yang Hua 0001, Tao Song 0003, Haibing Guan |
IWQoS | 1 |
| 2025 | Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment
Qingqian Yang, Peishen Yan, Jiaru Zhang, Tao Song 0003, Yang Hua 0001, Hao Wang 0022, Haibing Guan |
ICCV | 2 |
| 2024 | SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks
Peishen Yan, Hao Wang 0022, Tao Song 0003, Yang Hua 0001, Ruhui Ma, Ningxin Hu, Mohammad R. Haghighat, Haibing Guan |
ECCV (19) | 1 |
| 2024 | OFL-W3: A One-shot Federated Learning System on Web 3.0abstractFederated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning without compromising privacy or security. Concurrently, the advancement of blockchain technology and decentralized applications (DApps) within Web 3.0 heralds a new era of transformative possibilities in web development. As such, incorporating FL into Web 3.0 paves the path for overcoming the limitations of data silos through collaborative learning. However, given the transaction speed constraints of core blockchains such as Ethereum (ETH) and the latency in smart contracts, employing one-shot FL, which minimizes client-server interactions in traditional FL to a single exchange, is considered more apt for Web 3.0 environments. This paper presents a practical one-shot FL system for Web 3.0, termed OFL-W3. OFL-W3 capitalizes on blockchain technology by utilizing smart contracts for managing transactions. Meanwhile, OFL-W3 utilizes the Inter-Planetary File System (IPFS) coupled with Flask communication, to facilitate backend server operations to use existing one-shot FL algorithms. With the integration of the incentive mechanism, OFL-W3 showcases an effective implementation of one-shot FL on Web 3.0, offering valuable insights and future directions for AI combined with Web 3.0 studies. Linshan Jiang, Moming Duan, Bingsheng He, Peishen Yan, Yang Hua 0001, Tao Song 0003 |
Proc. VLDB Endow. | 5 |