In our June report “The Holy Grail of Crypto AI: Frontier Exploration of Decentralized Training”, we discussed Federated Learning—a “controlled decentralization” paradigm positioned between distributed training and fully decentralized training. Its core principle is keeping data local while aggregating parameters centrally, a design particularly suited for privacy-sensitive and compliance-heavy industries such as healthcare and finance.

At the same time, our past research has consistently highlighted the rise of Agent Networks. Their value lies in enabling complex tasks to be completed through autonomous cooperation and division of labor across multiple agents, accelerating the shift from “large monolithic models” toward “multi-agent ecosystems.”

Federated Learning, with its foundations of local data retention, contribution-based incentives, distributed design, transparent rewards, privacy protection, and regulatory compliance, has laid important groundwork for multi-party collaboration. These same principles can be directly adapted to the development of Agent Networks. The FedML team has been following this trajectory: evolving from open-source roots to TensorOpera (an AI infrastructure layer for the industry), and further advancing to ChainOpera (a decentralized Agent Network).

That said, Agent Networks are not simply an inevitable extension of Federated Learning. Their essence lies in autonomous collaboration and task specialization among agents, and they can also be built directly on top of Multi-Agent Systems (MAS), Reinforcement Learning (RL), or blockchain-based incentive mechanisms.

Federated Learning (FL) is a framework for collaborative training without centralizing data. Its core principle is that each participant trains a model locally and uploads only parameters or gradients to a coordinating server for aggregation, thereby ensuring “data stays within its domain” and meeting privacy and compliance requirements.

Having been tested in sectors such as healthcare, finance, and mobile applications, FL has entered a relatively mature stage of commercialization. However, it still faces challenges such as high communication overhead, incomplete privacy guarantees, and efficiency bottlenecks caused by heterogeneous devices.

Compared with other training paradigms:

• Distributed training emphasizes centralized compute clusters to maximize efficiency and scale.

• Decentralized training achieves fully distributed collaboration via open compute networks.

• Federated learning lies in between, functioning as a form of “controlled decentralization”: it satisfies industrial requirements for privacy and compliance while enabling cross-institution collaboration, making it more suitable as a transitional deployment architecture.
  

In our previous research, we categorized the AI Agent protocol stack into three major layers:

The foundational runtime support for agents, serving as the technical base of all Agent systems.

• Core Modules:

  • Agent Framework – development and runtime environment for agents.

  • Agent OS – deeper-level multitask scheduling and modular runtime, providing lifecycle management for agents.

• Supporting Modules:

  • Agent DID (decentralized identity)

  • Agent Wallet & Abstraction (account abstraction & transaction execution)

  • Agent Payment/Settlement (payment and settlement capabilities)

Focuses on agent collaboration, task scheduling, and incentive systems—key to building collective intelligence among agents.

• Agent Orchestration: Centralized orchestration and lifecycle management, task allocation, and workflow execution—suited for controlled environments.

• Agent Swarm: Distributed collaboration structure emphasizing autonomy, division of labor, and resilient coordination—suited for complex, dynamic environments.

• Agent Incentive Layer: Economic layer of the agent network that incentivizes developers, executors, and validators, ensuring sustainable ecosystem growth.

Covers distribution channels, end-user applications, and consumer-facing products.

• Distribution Sub-layer: Agent Launchpads, Agent Marketplaces, Agent Plugin Networks

• Application Sub-layer: AgentFi, Agent-native DApps, Agent-as-a-Service

• Consumer Sub-layer: Social/consumer agents, focused on lightweight end-user scenarios

• Meme Sub-layer: Hype-driven “Agent” projects with little actual technology or application—primarily marketing-driven.

FedML is one of the earliest open-source frameworks for Federated Learning (FL) and distributed training. Originating from an academic team at USC, it gradually evolved into the core product of TensorOpera AI through commercialization.

For researchers and developers, FedML provides cross-institution and cross-device tools for collaborative data training. In academia, FedML has become a widely adopted experimental platform for FL research, frequently appearing at top conferences such as NeurIPS, ICML, and AAAI. In industry, it has earned a strong reputation in privacy-sensitive fields such as healthcare, finance, edge AI, and Web3 AI—positioning itself as the benchmark toolchain for federated learning.

TensorOpera represents the commercialized evolution of FedML, upgraded into a full-stack AI infrastructure platform for enterprises and developers. While retaining its federated learning capabilities, it extends into GPU marketplaces, model services, and MLOps, thereby expanding into the broader market of the LLM and Agent era.

Its overall architecture is structured into three layers: Compute Layer (foundation), Scheduler Layer (coordination), and MLOps Layer (application).

1. Compute Layer (Foundation)
   The Compute layer forms the technical backbone of TensorOpera, continuing the open-source DNA of FedML.

   • Core Functions: Parameter Server, Distributed Training, Inference Endpoint, and Aggregation Server.

   • Value Proposition: Provides distributed training, privacy-preserving federated learning, and a scalable inference engine. Together, these support the three core capabilities of Train / Deploy / Federate, covering the full pipeline from model training to deployment and cross-institution collaboration.

2. Scheduler Layer (Coordination)
   The Scheduler layer acts as the compute marketplace and scheduling hub, composed of GPU Marketplace, Provision, Master Agent, and Schedule & Orchestrate modules.

   • Capabilities: Enables resource allocation across public clouds, GPU providers, and independent contributors.

   • Significance: This marks the pivotal step from FedML to TensorOpera—supporting large-scale AI training and inference through intelligent scheduling and orchestration, covering LLM and generative AI workloads.

   • Tokenization Potential: The “Share & Earn” model leaves an incentive mechanism interface open, showing compatibility with DePIN or broader Web3 models.

3. MLOps Layer (Application)
   The MLOps layer provides direct-facing services for developers and enterprises, including Model Serving, AI Agents, and Studio modules.

   • Applications: LLM chatbots, multimodal generative AI, and developer copilot tools.

In March 2025, TensorOpera upgraded into a full-stack platform oriented toward AI Agents, with its core products covering AgentOpera AI App, Framework, and Platform:

• Application Layer: Provides ChatGPT-like multi-agent entry points.

• Framework Layer: Evolves into an “Agentic OS” through graph-structured multi-agent systems and Orchestrator/Router modules.

• Platform Layer: Deeply integrates with the TensorOpera model platform and FedML, enabling distributed model services, RAG optimization, and hybrid edge–cloud deployment.

The overarching vision is to build “one operating system, one agent network”, allowing developers, enterprises, and users to co-create the next-generation Agentic AI ecosystem in an open and privacy-preserving environment.

If FedML represents the technical core, providing the open-source foundations of federated learning and distributed training; and TensorOpera abstracts FedML’s research outcomes into a commercialized, full-stack AI infrastructure—then ChainOpera takes this platform capability on-chain.

By combining AI Terminals + Agent Social Networks + DePIN-based compute/data layers + AI-Native blockchains, ChainOpera seeks to build a decentralized Agent Network ecosystem.

The fundamental shift is this: while TensorOpera remains primarily enterprise- and developer-oriented, ChainOpera leverages Web3-style governance and incentive mechanisms to include users, developers, GPU providers, and data contributors as co-creators and co-owners. In this way, AI Agents are not only “used” but also “co-created and co-owned.”

Through its Model & GPU Platform and Agent Platform, ChainOpera provides toolchains, infrastructure, and coordination layers for collaborative creation. This enables model training, agent development, deployment, and cooperative scaling.

The ecosystem’s co-creators include:

• AI Agent Developers – design and operate agents.

• Tool & Service Providers – templates, MCPs, databases, APIs.

• Model Developers – train and publish model cards.

• GPU Providers – contribute compute power via DePIN or Web2 cloud partnerships.

• Data Contributors & Annotators – upload and label multimodal datasets.

Together, these three pillars—development, compute, and data—drive the continuous growth of the agent network.

ChainOpera also introduces a co-ownership mechanism through shared participation in building the network.

• AI Agent Creators (individuals or teams) design and deploy new agents via the Agent Platform, launching and maintaining them while pushing functional and application-level innovation.

• AI Agent Participants (from the community) join agent lifecycles by acquiring and holding Access Units, thereby supporting agent growth and activity through usage and promotion.

These two roles represent the supply side and demand side, together forming a value-sharing and co-development model within the ecosystem.

ChainOpera collaborates widely to enhance usability, security, and Web3 integration:

• AI Terminal App combines wallets, algorithms, and aggregation platforms to deliver intelligent service recommendations.

• Agent Platform integrates multi-framework and low-code tools to lower the development barrier.

• TensorOpera AI powers model training and inference.

• FedML serves as an exclusive partner, enabling cross-institution, cross-device, privacy-preserving training.

The result is an open ecosystem balancing enterprise-grade applications with Web3-native user experiences.

Through DeAI Phones, wearables, and robotic AI partners, ChainOpera integrates blockchain and AI into smart terminals. These devices enable dApp interaction, edge-side training, and privacy protection, gradually forming a decentralized AI hardware ecosystem.

• TensorOpera GenAI Platform – provides full-stack services across MLOps, Scheduler, and Compute; supports large-scale model training and deployment.

• TensorOpera FedML Platform – enterprise-grade federated/distributed learning platform, enabling cross-organization/device privacy-preserving training and serving as a bridge between academia and industry.

• FedML Open Source – the globally leading federated/distributed ML library, serving as the technical base of the ecosystem with a trusted, scalable open-source framework.

In June 2025, ChainOpera officially launched its AI Terminal App and decentralized tech stack, positioning itself as a “Decentralized OpenAI.” Its core products span four modules:

1. Application Layer – AI Terminal & Agent Network

2. Developer Layer – Agent Creator Center

3. Model & GPU Layer – Model & Compute Network

4. CoAI Protocol & Dedicated Chain

Together, these modules cover the full loop from user entry points to underlying compute and on-chain incentives.

Already integrated with BNB Chain, the AI Terminal supports on-chain transactions and DeFi-native agents. The Agent Creator Center is open to developers, providing MCP/HUB, knowledge base, and RAG capabilities, with continuous onboarding of community-built agents. Meanwhile, ChainOpera launched the CO-AI Alliance, partnering with io.net, Render, TensorOpera, FedML, and MindNetwork.

According to BNB DApp Bay on-chain data (past 30 days): 158.87K unique users, 2.6M transactions and Ranked #2 in the entire “AI Agent” category on BSC, This demonstrates strong and growing on-chain activity.

Positioned as a decentralized ChatGPT + AI Social Hub, the AI Terminal provides: Multimodal collaboration, Data contribution incentives, DeFi tool integration, Cross-platform assistance, Privacy-preserving agent collaboration (Your Data, Your Agent). Users can directly call the open-source DeepSeek-R1 model and community-built agents from mobile. During interactions, both language tokens and crypto tokens circulate transparently on-chain.

Core Value: transforms users from “content consumers” into “intelligent co-creators.” Applicable across DeFi, RWA, PayFi, e-commerce, and other domains via personalized agent networks.

Envisioned as LinkedIn + Messenger for AI Agents.Provides virtual workspaces and Agent-to-Agent collaboration mechanisms (MetaGPT, ChatDEV, AutoGEN, Camel).Evolves single agents into multi-agent cooperative networks spanning finance, gaming, e-commerce, and research.Gradually enhances memory and autonomy.

Designed as a “LEGO-style” creation experience for developers.Supports no-code and modular extensions, Blockchain smart contracts ensure ownership rights, DePIN + cloud infrastructure lower entry barriers and Marketplace enables discovery and distribution

Core Value: empowers developers to rapidly reach users, with contributions transparently recorded and rewarded.

Serving as the infrastructure layer, it combines DePIN and federated learning to address Web3 AI’s reliance on centralized compute. Capabilities include:Distributed GPU network, Privacy-preserving data training, Model and data marketplace, End-to-end MLOps
Vision: shift from “big tech monopoly” to “community-driven infrastructure”—enabling multi-agent collaboration and personalized AI.

Beyond the already launched full-stack AI Agent platform, ChainOpera AI holds a firm belief that Artificial General Intelligence (AGI) will emerge from multimodal, multi-agent collaborative networks. Its long-term roadmap is structured into four phases:

Phase I (Compute → Capital):

• Build decentralized infrastructure: GPU DePIN networks, federated learning, distributed training/inference platforms.

• Introduce a Model Router to coordinate multi-end inference.

• Incentivize compute, model, and data providers with usage-based revenue sharing.

Phase II (Agentic Apps → Collaborative AI Economy):

• Launch AI Terminal, Agent Marketplace, and Agent Social Network, forming a multi-agent application ecosystem.

• Deploy the CoAI Protocol to connect users, developers, and resource providers.

• Introduce user–developer matching and a credit system, enabling high-frequency interactions and sustainable economic activity.

Phase III (Collaborative AI → Crypto-Native AI):

• Expand into DeFi, RWA, payments, and e-commerce scenarios.

• Extend to KOL-driven and personal data exchange use cases.

• Develop finance/crypto-specialized LLMs and launch Agent-to-Agent payments and wallet systems, unlocking “Crypto AGI” applications.

Phase IV (Ecosystems → Autonomous AI Economies):

• Evolve into autonomous subnet economies, each subnet specializing in applications, infrastructure, compute, models, or data.

• Enable subnet governance and tokenized operations, while cross-subnet protocols support interoperability and cooperation.

• Extend from Agentic AI into Physical AI (robotics, autonomous driving, aerospace).

Disclaimer: This roadmap is for reference only. Timelines and functionalities may adjust dynamically with market conditions and do not constitute a delivery guarantee.

ChainOpera has not yet released a full token incentive plan, but its CoAI Protocol centers on “co-creation and co-ownership.” Contributions are transparently recorded and verifiable via blockchain and a Proof-of-Intelligence (PoI) mechanism. Developers, compute providers, data contributors, and service providers are compensated based on standardized contribution metrics. Users consume services.Resource providers sustain operations.Developers build applications. All participants share in ecosystem growth dividends. The platform sustains itself via a 1% service fee, allocation rewards, and liquidity support—building an open, fair, and collaborative decentralized AI ecosystem.

PoI is ChainOpera’s core consensus mechanism under the CoAI Protocol, designed to establish a transparent, fair, and verifiable incentive and governance system for decentralized AI.  It extends Proof-of-Contribution into a blockchain-enabled collaborative machine learning framework, addressing federated learning’s persistent issues: insufficient incentives, privacy risks, and lack of verifiability.

Core Design:

• Anchored in smart contracts, integrated with decentralized storage (IPFS), aggregation nodes, and zero-knowledge proofs (zkSNARKs).

• Achieves five key objectives:

  1. Fair rewards based on contribution, ensuring trainers are incentivized for real model improvements.

  2. Data remains local, guaranteeing privacy protection.

  3. Robustness mechanisms against malicious participants (poisoning, aggregation attacks).

  4. ZKP verification for critical processes: model aggregation, anomaly detection, contribution evaluation.

  5. Efficiency and generality across heterogeneous data and diverse learning tasks.

ChainOpera’s token design is anchored in utility and contribution recognition, not speculation. It revolves around five core value streams:

• LaunchPad – for agent/application initiation.

• Agent API – service access and integration.

• Model Serving – inference and deployment fees.

• Contribution – data annotation, compute sharing, or service input.

• Model Training – distributed training tasks.

Stakeholders:

• AI Users – spend tokens to access services or subscribe to apps; contribute by providing/labeling/staking data.

• Agent & App Developers – use compute/data for development; rewarded for contributing agents, apps, or datasets.

• Resource Providers – contribute compute, data, or models; rewarded transparently.

• Governance Participants (Community & DAO) – use tokens to vote, shape mechanisms, and coordinate the ecosystem.

• Protocol Layer (CoAI) – sustains development through service fees and automated balancing of supply/demand.

• Nodes & Validators – secure the network by providing validation, compute, and security services.

ChainOpera adopts DAO-based governance, where token staking enables participation in proposals and voting, ensuring transparency and fairness.

Governance mechanisms include:

• Reputation System – validates and quantifies contributions.

• Community Collaboration – proposals and voting drive ecosystem evolution.

• Parameter Adjustments – covering data usage, security, and validator accountability.

The overarching goal: prevent concentration of power, ensure system stability, and sustain community co-creation.

The ChainOpera project was co-founded by Professor Salman Avestimehr, a leading scholar in federated learning, and Dr. Aiden Chaoyang He. The core team spans academic and industry backgrounds from institutions such as UC Berkeley, Stanford, USC, MIT, Tsinghua University, and tech leaders including Google, Amazon, Tencent, Meta, and Apple. The team combines deep research expertise with extensive industry execution capabilities and has grown to over 40 members to date.

• Title & Roles: Dean’s Professor of Electrical & Computer Engineering at University of Southern California (USC), Founding Director of the USC-Amazon Center on Trusted AI, and head of the vITAL (Information Theory & Machine Learning) Lab at USC.

• Entrepreneurship: Co-Founder & CEO of FedML, and in 2022 co-founded TensorOpera/ChainOpera AI.

• Education & Honors: Ph.D. in EECS from UC Berkeley (Best Dissertation Award). IEEE Fellow with 300+ publications in information theory, distributed computing, and federated learning, cited over 30,000 times. Recipient of PECASE, NSF CAREER Award, and the IEEE Massey Award, among others.

• Contributions: Creator of the FedML open-source framework, widely adopted in healthcare, finance, and privacy-preserving AI, which became a core foundation for TensorOpera/ChainOpera AI.

• Title & Roles: Co-Founder & President of TensorOpera/ChainOpera AI; Ph.D. in Computer Science from USC; original creator of FedML.

• Research Focus: Distributed & federated learning, large-scale model training, blockchain, and privacy-preserving computation.

• Industry Experience: Previously held R&D roles at Meta, Amazon, Google, Tencent; served in core engineering and management positions at Tencent, Baidu, and Huawei, leading the deployment of multiple internet-scale products and AI platforms.

• Academic Impact: Published 30+ papers with 13,000+ citations on Google Scholar. Recipient of the Amazon Ph.D. Fellowship, Qualcomm Innovation Fellowship, and Best Paper Awards at NeurIPS and AAAI.

• Technical Contributions: Led the development of FedML, one of the most widely used open-source frameworks in federated learning, supporting 27 billion daily requests. Core contributor to FedNLP and hybrid model parallel training methods, applied in decentralized AI projects such as Sahara AI.

In December 2024, ChainOpera AI announced the completion of a $3.5M seed round, bringing its total funding (combined with TensorOpera) to $17M. Funds will be directed toward building a blockchain Layer 1 and AI operating system for decentralized AI Agents.

• Lead Investors: Finality Capital, Road Capital, IDG Capital

• Other Participants: Camford VC, ABCDE Capital, Amber Group, Modular Capital

• Strategic Backers: Sparkle Ventures, Plug and Play, USC

• Notable Individual Investors:Sreeram Kannan, Founder of EigenLayer and David Tse, Co-Founder of BabylonChain

The team stated that this round will accelerate its vision of creating a decentralized AI ecosystem where resource providers, developers, and users co-own and co-create.

The federated learning (FL) field is shaped by four main frameworks. FedML is the most comprehensive, combining FL, distributed large-model training, and MLOps, making it enterprise-ready. Flower is lightweight and widely used in teaching and small-scale experiments. TFF (TensorFlow Federated) is academically valuable but weak in industrialization. OpenFL targets healthcare and finance, with strong compliance features but a closed ecosystem. In short: FedML is the industrial-grade all-rounder, Flower emphasizes ease of use, TFF remains academic, and OpenFL excels in vertical compliance.

TensorOpera, the commercialized evolution of FedML, integrates cross-cloud GPU scheduling, distributed training, federated learning, and MLOps in a unified stack. Positioned as a bridge between research and industry, it serves developers, SMEs, and Web3/DePIN ecosystems. Effectively, TensorOpera is like “Hugging Face + W&B” for federated and distributed learning, offering a more complete and general-purpose platform than tool- or sector-specific alternatives.

ChainOpera and Flock both merge FL with Web3 but diverge in focus. ChainOpera builds a full-stack AI Agent platform, turning users into co-creators through the AI Terminal and Agent Social Network. Flock centers on Blockchain-Augmented FL (BAFL), stressing privacy and incentives at the compute and data layer. Put simply: ChainOpera emphasizes applications and agent networks, while Flock focuses on low-level training and privacy-preserving computation.

Federated Learning & AI Infrastructure Landscape

At the agent-network level, the most representative projects are ChainOpera and Olas Network.

• ChainOpera: rooted in federated learning, builds a full-stack loop across models, compute, and agents. Its Agent Social Network acts as a testbed for multi-agent interaction and social collaboration.

• Olas Network (Autonolas / Pearl): originated from DAO collaboration and the DeFi ecosystem, positioned as a decentralized autonomous service network. Through Pearl, it delivers direct-to-market DeFi agent applications—showing a very different trajectory from ChainOpera.
  

• Technical Moat: ChainOpera’s strength lies in its unique evolutionary path: from FedML (the benchmark open-source framework for federated learning) → TensorOpera (enterprise-grade full-stack AI infrastructure) → ChainOpera (Web3-enabled agent networks + DePIN + tokenomics). This trajectory integrates academic foundations, industrial deployment, and crypto-native narratives, creating a differentiated moat.

• Applications & User Scale: The AI Terminal has already reached hundreds of thousands of daily active users and a thriving ecosystem of 1,000+ agent applications. It ranks #1 in the AI category on BNBChain DApp Bay, showing clear on-chain user growth and verifiable transaction activity. Its multimodal scenarios, initially rooted in crypto-native use cases, have the potential to expand gradually into the broader Web2 user base.

• Ecosystem Partnerships: ChainOpera launched the CO-AI Alliance, partnering with io.net, Render, TensorOpera, FedML, and MindNetwork to build multi-sided network effects across GPUs, models, data, and privacy computing. In parallel, its collaboration with Samsung Electronics to validate mobile multimodal GenAI demonstrates expansion potential into hardware and edge AI.

• Token & Economic Model: ChainOpera’s tokenomics are based on the Proof-of-Intelligence consensus, with incentives distributed across five value streams:

1. Technical execution risks: ChainOpera’s proposed five-layer decentralized architecture spans a wide scope. Cross-layer coordination—especially in distributed inference for large models and privacy-preserving training—still faces performance and stability challenges and has not yet been validated at scale.

2. User and ecosystem stickiness: While early user growth is notable, it remains to be seen whether the Agent Marketplace and developer toolchain can sustain long-term activity and high-quality contributions. The current Agent Social Network is mainly LLM-driven text dialogue; user experience and retention still need refinement. Without carefully designed incentives, the ecosystem risks short-term hype without long-term value.

3. Sustainability of the business model: At present, revenue primarily depends on platform service fees and token circulation; stable cash flows are not yet established. Compared with AgentFi or Payment-focused applications that carry stronger financial or productivity attributes, ChainOpera’s current model still requires further validation of its commercial value. In addition, the mobile and hardware ecosystem remains exploratory, leaving its market prospects uncertain.

Disclaimer: This report was prepared with assistance from AI tools (ChatGPT-5). The author has made every effort to proofread and ensure accuracy, but some errors or omissions may remain. Readers should note that crypto asset markets often exhibit divergence between project fundamentals and secondary-market token performance. This report is intended solely for information consolidation and academic/research discussion. It does not constitute investment advice, nor should it be interpreted as a recommendation to buy or sell any token.