2027 2nd International Conference on Blockchain Technology and Foundation Models(BTFM 2027)
HOME > Call For Papers
 
Call for Papers
Topics of interest include, but are not limited to:
We invite high-quality original research papers on a wide range of topics in blockchain technology and foundation models. Submissions should present novel contributions, theoretical insights, or practical applications that advance the state of the art in these rapidly evolving fields.
Blockchain Technology
Blockchain protocols and architectures Consensus algorithms Blockchain for IoT and edge computing Smart contracts and formal verification Privacy and anonymity Blockchain security and cryptography Decentralized identity and authentication Blockchain in finance and DeFi Energy‑efficient blockchain Cross‑chain interoperability Zero‑knowledge proofs and privacy-preserving Tokenomics and crypto‑economics Decentralized autonomous organizations (DAOs) Scalability solutions (sharding, rollups, sidechains) Blockchain governance and regulation Digital asset tokenization Blockchain for supply chain management Data provenance and traceability Decentralized storage systems Federated learning and blockchain integration Blockchain for healthcare and digital twins Post‑quantum cryptography in blockchain
… and related topics
Foundation Models
Large‑scale model architectures and training Multimodal models (vision, language, speech) Scalable pretraining and self‑supervised learning Domain adaptation and transfer learning Responsible and ethical AI Knowledge reasoning and commonsense Robustness, fairness and bias mitigation Model compression and efficient inference Real‑world applications and deployment Security and privacy in foundation models Few‑shot and zero‑shot learning Meta‑learning and continual learning Reinforcement learning with foundation models Agent‑based systems and LLM‑driven autonomy Multilingual and cross‑lingual models Model interpretability and explainability Efficient fine‑tuning (LoRA, adapters, PEFT) Foundation models for scientific discovery Retrieval‑augmented generation (RAG) Long‑context modeling and memory management Evaluation benchmarks and datasets Energy‑efficient training and green AI
… and related topics