Decentralized AI training platform, FLock.io announced that two professors from Korea University—cybersecurity specialist Junghee Lee and AI-driven biopharmaceutical researcher Jaewoo Kang—have officially joined the advisory board of the FLock Foundation. Their involvement is intended to support the foundation in strengthening system-level security and expanding its research applications within scientific contexts.
Professor Junghee Lee, a faculty member in the School of Cybersecurity at Korea University, holds a Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology. He previously completed his undergraduate and graduate studies in Computer Engineering at Seoul National University. His career includes roles at Samsung Electronics, where he worked on system-level design for mobile system-on-chip architectures, and at the University of Texas at San Antonio, where he served on the computer engineering faculty. His current research focuses on secure processor design, hardware-assisted security mechanisms, non-volatile memory technologies, and specialized hardware for trustworthy and efficient computation. These areas directly align with FLock.io’s mission to develop privacy-preserving and verifiable federated learning systems capable of functioning across distributed and diverse hardware infrastructures.
Professor Jaewoo Kang, also at Korea University in the Department of Computer Science and Engineering, serves as the co-founder and CEO of AIGEN Sciences, a company that applies generative AI techniques to drug discovery. He is internationally known for co-developing BioBERT, a domain-specific transformer model for biomedical applications, which has become a widely cited foundational model in the field of biomedical natural language processing. BioBERT has contributed to significant advances in biomedical tasks such as entity recognition, relation extraction, and question answering. Beyond academia, Professor Kang has led highly ranked teams in international biomedical AI competitions such as BioASQ and the DREAM Challenge, highlighting his ability to translate scientific research into impactful real-world applications. His expertise is expected to support FLock.io’s development of decentralized machine learning models tailored for secure, collaborative, and privacy-conscious research in the biomedical sector. The contributions of both professors are expected to reinforce FLock.io’s infrastructure, particularly in enhancing security mechanisms and furthering the applicability of federated learning in complex, high-stakes research environments.
FLock.io: Combining Federated Learning And Blockchain For Secure, Collaborative AI Model Development
FLock.io is a decentralized platform for AI model training that integrates federated learning with blockchain infrastructure. Its architecture is designed to support secure and privacy-preserving AI model development while promoting collaborative participation and decentralized governance. The platform incorporates several key components, including AI Arena for distributed model training, FL Alliance for federated model fine-tuning, and an AI Marketplace dedicated to deployment and distribution. All activities within this ecosystem are coordinated through the native FLOCK token, which serves functions related to staking, validation, and protocol governance.
The system allows participants to train and refine models without requiring centralized access to sensitive data, thereby preserving data sovereignty and minimizing privacy risks. Contributors are compensated through on-chain mechanisms, with rewards tied to performance and participation. The platform’s operational structure is reinforced by a proof-of-stake framework that includes slashing penalties for misconduct, promoting integrity across its decentralized infrastructure. The focus remains on federated learning workflows, community-driven governance, and scalable deployment through the AI Marketplace.
Recently, FLock.io launched the Elite Trainer Program. This initiative is aimed at engaging highly skilled machine learning professionals to act as training nodes within the decentralized AI Training Arena, expanding both the technical capabilities and reliability of its federated learning network.
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