Research
The research achievements of the FLock team have been published in top conferences and journals such as NeurIPS, ICML, IEEE, Science, and ACL, and have won multiple Best Paper Awards.
AllPublications
Showing 17 of 17 publications
A Control Theoretic Approach to Decentralized AI Economy Stabilization via Dynamic Buyback-and-Burn Mechanisms
Dynamic buyback-and-burn stabilizes the DeAI economy layer, mitigating native-token volatility under network demand shocks.
Identity-decoupled Anonymization for Visual Evidence in RAG
Decouples identity signals from visual evidence used in retrieval-augmented generation pipelines.
GraphSynth: Resolving the Diversity-Reliability Trade-off in Synthetic Data Generation
Resolves the diversity-reliability trade-off in AI synthetic data via probabilistic factor graphs. Accepted to ACL 2026.
AgentaNet: Decentralized Agent Swarm Network
Coordination layer for decentralized swarms of AI agents — economic primitives, routing, and consensus mechanisms.
Multi-continental Healthcare Modelling using Blockchain-enabled Federated Learning
Five hospitals across Europe, North America, and Asia jointly train a clinical-grade model. Published at IEEE Blockchain 2025.
Scaling Distributed Learning with FLock
First industry deployment of decentralized fine-tuning for a 70B-parameter LLM. Data stays on-prem while gradients flow through the network.
On Weaponization-Resistant Large Language Models with Prospect Theoretic Alignment
Prospect-theoretic alignment technique to make large language models robust against weaponization attempts.
GeoLifeCLEF 2024 Challenge: Plant Species Distribution Prediction
Plant species distribution prediction challenge leveraging federated learning over geographically distributed data.
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach
Asynchronous decentralized federated learning for cross-prediction of blood glucose levels while preserving patient privacy.
Learn Moreabout FLock
FLock: Federated Machine Learning on Blockchain
Democratising AI through Decentralisation of Data, Computation and Models
Federated Machine Learning on Blockchain
Facilitating an open and collaborative environment where participants can contribute models, data, and computing resources, in exchange for on-chain rewards based on their traceable contributions.