Welcome to the 0G Research

At 0G, we bridge the gap between advanced AI and Web3 technologies. Our research department is dedicated to pioneering breakthroughs in decentralized AI, fostering collaboration, and driving innovation across blockchain ecosystems. Below are the major research areas we are currently pursuing.

Optimization of Model Training in Decentralized AI Systems

In this task, we aim to design a suite of methodologies and frameworks at both the system and algorithm levels, to boost training efficiency in large-scale decentralized environments. Specifically, we will consider the following optimization directions:

Communication Optimization

Decentralized learning requires participating nodes to frequently share intermediate training results via networks. Larger models and data could result in huge communication costs, affecting overall training efficiency. We will introduce cutting-edge solutions to reduce such overhead, including lossless gradient compression and quantization.

Local Computation Optimization

Training models at scale incurs significant computation overhead and resource consumption. This becomes a bottleneck for local devices in decentralized AI. We aim to alleviate such pressure from two perspectives: integrating advanced training pipeline and parallelization technologies to accelerate the training process, and pruning the models to reduce computation, thus realizing efficient training at the edge.

Performance Optimization with Heterogeneous Data

Data heterogeneity across different nodes in decentralized systems can deteriorate model accuracy due to local model drift. To fix this issue in practical data-heterogeneity scenarios, we will design novel algorithms for training and aggregation. For instance, we can adaptively adjust the learning rate of each neuron based on local data to rectify model drift, and perform meticulous round selection for more robust aggregation.

Performance Optimization with Dynamic Environment

In a practical decentralized AI system, nodes can join or exit the collaborative tasks in an unpredictable way. Robust solutions are required to handle such dynamic features while guaranteeing model performance. To this end, we will design new asynchronous learning protocols and schedulers to efficiently and accurately predict node participations, and select high-quality updates from certain nodes and iterations for enhanced aggregation.

Model Alignment in Decentralized AI Systems

Contemporary large language models demonstrate exceptional text interpretation and generation capabilities. However, they also raise ethical risks as they could inadvertently generate inappropriate, biased, harmful, or non-factual content. These risks are exacerbated in decentralized AI ecosystems, where each node’s training data is neither controllable nor filtered. It is important to perform model alignment, ensuring the model output aligns with human values. We will make several efforts toward this task.

Enhanced Learning Algorithms with Human Preference

The most common solution for alignment is to integrate human preferences as human values in model optimization, e.g., Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). We aim to apply these strategies to decentralized settings and make them more efficient. Possible improvements include the construction of higher-quality preference datasets, design of advanced reward functions, and distributed sharing of human feedback across different participating nodes.

Self-Regulation and Correction

In social psychology, perspective taking is an important emotional intelligence skill, inspiring individuals to leverage self-awareness for behavior regulation. Inspired by this principle, we will propose new alignment strategies, which guide the model to automatically inspect its output responses, identify any content misaligned with human values, and rectify it. A new end-to-end regulation and correction pipeline with advanced prompts will be established to achieve this goal.

Decentralized Debating for Alignment

Debating is another popular alignment method, where multiple models (or agents) debate with each other to produce the most accurate and valuable content. This approach is a natural fit for decentralized AI, where there are multiple models from different nodes ready for debate. We will implement this strategy in real-world, large-scale decentralized scenarios and make adaptations to enhance alignment efficiency and effectiveness.

New Blockchain System Empowered by Multi-Agent Technology

LLM-based multi-agent systems are becoming prevalent due to their excellent capability of handling complex tasks. Such systems coordinate distributed agents—each with specialized AI models—to collaborate on any given task. The characteristics of these systems align well with blockchain environments, indicating a significant potential for integration to enhance both functionality and efficiency. Specifically, each node in a blockchain can be implemented as an agent to establish a multi-agent setup. Following this principle, we will consider several applications for multi-agent-based blockchain:

Smart Contract Management

Smart contracts are a critical component in blockchain to automate transaction execution. It is promising to analyze, manage, and optimize this software in a distributed manner. We could implement a multi-agent solution in the blockchain, with each agent focusing on different functionalities. Their collaboration could significantly augment the blockchain with comprehensive smart contract services.

Anomaly Detection

During blockchain execution, malicious entities may attempt to interfere with transactions, consensus mechanisms, or cross-node communications. It is thus vital to introduce security schemes to monitor the system and detect any anomalies. We will design and develop multi-agent systems to achieve this goal. By encouraging different agents to focus on various aspects of events and coordinating their decisions, the trustworthiness of the blockchain environment will be greatly enhanced.

Recent Publications

Below is one of our latest publications that showcases our ongoing research:

BadSFL: Backdoor Attack against Scaffold Federated Learning

Authors: Xingshuo Han, Xuanye Zhang, Xiang Lan, Haozhao Wang, Shengmin Xu, Shen Ren, Jason Zeng, Ming Wu, Michael Heinrich, Tianwei Zhang
Year: 2024
arXiv ePrint: 2411.16167
URL: https://arxiv.org/abs/2411.16167

BibTex Citation

@misc{han2024badsflbackdoorattackscaffold,
  title={BadSFL: Backdoor Attack against Scaffold Federated Learning}, 
  author={Xingshuo Han and Xuanye Zhang and Xiang Lan and Haozhao Wang and Shengmin Xu and Shen Ren and Jason Zeng and Ming Wu and Michael Heinrich and Tianwei Zhang},
  year={2024},
  eprint={2411.16167},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2411.16167},
}