Collaborative Intelligence: Performant Blockchains Powering Private, Democratically-Governed AI
Federated Learning (FL) and blockchain technology are emerging as a powerful combination for building decentralized AI systems. In FL, multiple devices or organizations collaboratively train an AI model without sharing their raw data, preserving privacy. A blockchain can provide a tamper-proof, decentralized infrastructure to coordinate this training and govern the resulting public AI models. However, to make this synergy practical at scale, the blockchain must handle an extremely high throughput such as Sei Giga in the order of 5 gigagas per second. In this article, we explore why federated learning is needed, how public AI models can be governed via blockchain, how blockchain-based FL compares to traditional approaches, and why a high-throughput 5 gigagas blockchain is essential for real-time, large-scale federated learning.
Federated learning is a distributed machine learning approach where model training happens across many devices or silos of data, instead of requiring all data to be pooled on a central server. The primary motivation is privacy: large datasets have driven AI breakthroughs, but much data is sensitive or siloed, such as personal smartphone data, hospital records, or proprietary business information. FL enables collaboratively training a model on data from multiple users or organizations without any raw data ever leaving their devices or local servers. By keeping data local, FL prevents most forms of data leakage and avoids the need to trust a central data holder with private information. Beyond privacy, federated learning unlocks large-scale collaboration in AI. Instead of being limited to one organization’s dataset, a model can learn from billions of distributed devices like phones, IoT sensors, or vehicles, or from multiple institutions at once. For example, smartphones can jointly train a next-word prediction model for a keyboard app without sending their message histories to the cloud, and hospitals can cooperatively train medical AI models on patient outcomes across the world without violating confidentiality. In essence, FL makes it possible to harness vastly more data and edge computing power than any single centralized system, enabling large-scale aggregation and modeling of complicated systems while each participant maintains ownership of their data.
Public AI models that emerge from such collaborative training are not owned or controlled by any single entity but are a product of collective contribution. These models hold tremendous value and impact, which raises the question of governance: who decides how the model is used, updated, or regulated? Traditional governance of AI models typically falls to either a central corporate authority or a small committee of organizers. However, centralized control of a widely used model can lead to conflicts of interest, lack of transparency, and potential misuse or bias. A single company might prioritize profit or suppress certain features that the community wants, and without broader oversight, there is a risk of extreme concentration of capital, computing resources, and data in AI development. Users must trust that the central authority will not abuse their power, censor information, or neglect certain stakeholders’ needs, yet there is often little recourse if decisions are misaligned with the public interest.
A decentralized governance approach, enabled by blockchain technology, offers a solution. By putting the model’s governance rules and decision processes on a public blockchain, control can be distributed among the community of stakeholders. With decentralized governance, there is no unified authority in the system, and individual players cannot dominate or manipulate operations. Every stakeholder, data provider, model user, and domain expert, could have a vote on decisions such as model updates, parameter tuning, or usage policies. Blockchain-based governance can leverage smart contracts or on-chain voting mechanisms, making decisions transparent and tamper-proof, as all governance actions are recorded immutably on the ledger. This approach involves more stakeholders at different stages of the process, ensuring that diverse perspectives are included and no middlemen are needed to mediate trust. By applying a self-sovereign mindset to AI, decentralized governance redistributes power and mitigates concerns over privacy, equity, and accessibility. In a blockchain-based governance model, the community can encode ethical guidelines or access rules directly into the smart contracts governing the model. Blockchains also enable ownership stakes in the model to be represented via tokens or NFTs, giving contributors proportional say in the model's direction rather than concentrating control in a single organization. Overall, decentralized blockchain governance provides transparency, collective decision-making, and resilience against unilateral control, ensuring that as AI models become critical public infrastructure, their policies and behavior remain accountable to the communities they serve.
Early federated learning implementations relied on a central server or aggregator to coordinate the training. In the traditional model, a central server would send the current model to all clients, aggregate their updates, and broadcast the updated global model. While the data is decentralized, this approach introduces a central point of trust and failure. Some non-blockchain approaches attempt to remove the central server by using peer-to-peer protocols or reputation systems, but these face challenges such as vulnerability to fake identities and collusion among reputed nodes. In contrast, integrating blockchain transforms FL into a fully decentralized process. Instead of sending updates to a central server, clients submit their model updates, or cryptographic commitments of updates, as transactions to the blockchain. A network of blockchain nodes, acting as aggregators, maintains the global model state, while smart contracts define the logic of aggregation such as averaging weights and validating updates in a transparent, autonomous manner. Because the blockchain is a tamper-proof ledger, all model updates and training interactions are recorded immutably, allowing any participant to verify that their update was correctly incorporated and to inspect the history of the model’s evolution. No single point exists where a malicious actor can secretly alter the training process; any attempt at manipulation is visible on-chain and mitigated by consensus.
Replacing the central server with a decentralized ledger reduces the risk of a single point of failure, as no single entity can compromise the model or halt the training. An attacker would need to control a majority of blockchain nodes to tamper with the process, a far more challenging prospect than hacking a lone server. The blockchain’s cryptographic mechanisms, digital signatures and hash linking ensure that updates cannot be forged or altered unnoticed, drastically improving security and eliminating the need to trust any central party. All updates, model parameters, and decisions are logged on-chain, which provides an open, auditable record of the training process. This transparency makes it easy to trace any anomalies or biases back through the blockchain record. With decentralized governance, the rules of training can be collectively decided and encoded in the protocol, ensuring that every participant operates on a level playing field. Moreover, blockchain-based systems can integrate native incentive mechanisms using tokens or cryptocurrencies. By automatically rewarding clients for useful model updates, blockchain smart contracts create economic incentives for honest participation, while also providing mechanisms to penalize malicious behavior.
A critical enabler for this entire approach is throughput. Federated learning in a large-scale environment may involve hundreds of thousands or even millions of participants, each transmitting frequent updates to the global model. Traditional blockchains like Bitcoin or Ethereum can only handle a few tens to a few thousand transactions per second, which is insufficient for real-time model training. For instance, an FL system with 100,000 participants that posts an update once per training round, and completes each round in five seconds, requires processing around 20,000 updates per second. A high-throughput blockchain capable of processing on the order of 5 gigagas per second can process over 200k transactions per second. This level of performance is comparable to centralized systems and is essential for ensuring that model updates are aggregated and disseminated in near real-time. Low latency finality is equally important; if each training round’s updates are confirmed in under a second, the overall training loop remains efficient. Without such performance, the blockchain becomes a bottleneck, undermining the benefits of federated learning.
High throughput also enables sophisticated incentive mechanisms. In a decentralized setting, every transaction whether it’s a model update or a reward payment adds to the total load. A blockchain operating at 5 gigagas per second can manage not only the core training transactions but also thousands of micropayments that reward honest behavior and penalize bad actors. This real-time incentive system is crucial for sustaining continuous, high-quality participation. The economic model built into the blockchain ensures that rewards and penalties are distributed automatically and transparently, encouraging prompt and reliable contributions from all nodes.
In summary, federated learning offers a promising way to train AI models collaboratively while preserving privacy, but it requires a robust, decentralized infrastructure to overcome the limitations of central aggregators. A blockchain-based approach provides the security, transparency, and decentralized governance necessary for public AI models, ensuring that no single entity controls the training process. However, to make this vision a reality on a global scale, the underlying blockchain must deliver unprecedented throughput on the order of 5 gigagas per second to handle the massive volume of transactions generated by real-time federated learning. This combination of decentralized training and high-performance blockchain technology paves the way for AI systems that are both highly scalable and democratically governed, moving us closer to a future where blockchain-empowered AI is fast, secure, and fair for all.
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