Knit Litepaper

Threading AI, Together.

Abstract

In the contemporary digital landscape, Machine Learning (ML) is at the forefront of transformative innovation. However, the training of ML models presents formidable challenges, primarily due to the significant computational demands and privacy concerns. This paper presents an advanced solution that combines computational efficiency and privacy preservation—the Knit Protocol, a truly decentralized approach to ML.

Unlike conventional Federated Learning (FL) systems, which are still dependent on a central server for model aggregation, the Knit Protocol fundamentally revolutionizes the process. It fully decentralizes all operations, eliminating the requirement for a central server, or any centralization for that matter. Furthermore, it democratizes participation by enabling anyone with a connected device to contribute to the computational tasks.

The Knit Protocol supports a wide range of devices, from Internet of Things (IoT) devices and ASICs to Raspberry Pi, personal computers, and even cell phones. This expansive device compatibility ensures that individuals and organizations can leverage their existing hardware resources to contribute to the computational tasks, making ML training more accessible and scalable than ever before.

At the heart of the Knit Protocol is a unique integration of a Directed Acyclic Graph (DAG) and a multi-dimensional blockchain structure. This innovative combination underpins our protocol and ensures optimal task scheduling, effective load balancing, and robustness against failures. Additionally, this integration allows for an efficient, secure, and seamless experience when incorporated with existing blockchains, enhancing the Web3 ecosystem with improved scalability and data privacy.

This litepaper offers a comprehensive exploration of the Knit Protocol's architecture, its distinctive capabilities in facilitating a truly decentralized ML ecosystem, and its future developmental visions. As an innovative distributed computing platform, the Knit Protocol is designed to significantly enhance the efficiency and privacy of ML applications, while significantly reducing the costs, thereby leading the way towards a new era of digital innovation.

1. Introduction

Machine Learning (ML) stands as a transformative tool in the era of Industry 4.0, where the digital, physical, and biological worlds intertwine. ML leverages data, the oil of the digital era, to drive decision-making processes, automate mundane tasks, predict future trends, and uncover previously unobserved patterns. However, ML applications, particularly the training of deep learning models, demand substantial computational resources. Given the escalating growth in the volume, velocity, and variety of data, coupled with increasingly complex ML architectures, this computational demand is only set to surge.

One method to meet this demand is through cloud-based services, which offer scalable compute resources.As regulations on data privacy tighten, alternatives that ensure data security without compromising on computational power are necessary.

Enter Federated Learning (FL)—a decentralized approach to machine learning that trains an algorithm across multiple devices or servers holding local data samples, without exchanging them. FL can alleviate privacy concerns as raw data does not need to be centralized. However, for FL to be effective and efficient, it necessitates a robust, scalable, secure, and privacy-preserving computational infrastructure.

This is where the Knit Protocol steps in. The Knit Protocol offers a distributed compute platform specially tailored for executing FL in a secure and efficient manner. It employs a Directed Acyclic Graph (DAG) for intelligent task scheduling, fosters distributed storage and computation for resilience against failures, and ensures load balancing for optimal resource utilization.

The subsequent sections of this paper delve into the Knit Protocol's architecture, its functionalities, and capabilities in the context of FL. It presents an academic discussion on the state-of-the-art and opens avenues for future enhancements to further align with the evolving demands of ML applications. The insights and discussions presented here are of value to academia, industry, and policymakers alike, given the transdisciplinary nature of ML and its wide-ranging implications.

2. The Knit Protocol Architecture

The Knit Protocol is composed of a unique set of components that form an intricate structure designed for distributed ML computations with emphasis on privacy and efficiency. Here, we dissect its architecture to better understand how these components harmonize together.

2.1 Directed Acyclic Graph (DAG)

The Knit Protocol harnesses the capabilities of Directed Acyclic Graphs (DAG) for a swift and scalable management of computational tasks. This sophisticated technique, inspired by computer science and mathematics, enables parallel processing of tasks, resulting in increased throughput and reduced processing times. In the DAG configuration, each node corresponds to a computational task, with edges representing their execution sequence. This allows for simultaneous processing of multiple tasks, delivering unmatched speed and scalability that transcends traditional blockchain limitations.

In the context of the Knit Protocol, DAGs allow for the organization of tasks in an order that guarantees the most efficient use of resources and time, minimizes the waiting time between dependent tasks, and ensures the integrity of the computation. Moreover, it enables the system to deal with ML workloads that often comprise complex tasks with many interdependency.

2.2 Distributed Storage

The Knit Protocol employs distributed storage systems to ensure robustness and resilience of data against failures. In this setup, data is partitioned across multiple nodes in the network, each of which operates independently. This configuration facilitates the simultaneous processing of data, hence reducing latency and enhancing computational efficiency.

Moreover, by decentralizing data storage, the protocol provides an additional layer of security. The data partitioning makes it exceedingly difficult for malicious entities to gain complete access to datasets, further protecting the privacy of data.

2.2.1 Consensus in Distributed Storage

In distributed systems, the concept of consensus is essential for ensuring system reliability and data integrity. The Raft consensus algorithm is one such protocol that is often employed for managing replicated logs across distributed nodes.

The Raft protocol is designed to be straightforward to understand, while also ensuring system availability in the event of individual node failures. It guarantees that all changes to the replicated log are reflected across all the nodes in the distributed system, thus maintaining data consistency.

Raft accomplishes this through a leader-based system. At any given time, one node is elected as the leader, and the leader is responsible for managing log replication. The leader accepts log entries from clients, appends the entries to its local log, and then propagates these entries to its follower nodes.

2.2.2 Raft and DAG Interplay in the Knit Protocol

The Knit Protocol employs the Raft consensus algorithm in conjunction with a Directed Acyclic Graph (DAG) to establish a coherent order of transactions and maintain data integrity. While the DAG serves as a data structure capturing the partial order of events in a distributed system, the Raft algorithm is used to reach consensus on the order of these events.

In this setting, the DAG captures the dependencies between tasks or transactions in the system, with its edges reflecting a "happens-before" relationship. Each node in the DAG represents a task or transaction, and a directed edge signifies that one task must occur before another.

However, due to the distributed nature of the system, tasks might be initiated at different nodes almost simultaneously leading to concurrent branches in the DAG. This is where the Raft consensus algorithm comes into play. The Raft algorithm is leveraged to decide on a total order among these concurrent branches to ensure consistency across the network.

A leader is elected through the Raft protocol, responsible for ordering the conflicting branches in the DAG and distributing this order back to the other nodes in the system. By ordering the events and resolving conflicts, the leader ensures that all nodes agree on a single version of the truth, thereby preserving the accuracy and reliability of data.

The combination of DAG and the Raft consensus algorithm in the Knit protocol allows efficient parallel processing of non-conflicting tasks while ensuring data consistency across all nodes in the network. This strategy significantly enhances the throughput and overall performance of the system.

2.2.3 Security and Raft

The Raft protocol inherently provides security benefits for the Knit Protocol's distributed storage system. As mentioned earlier, Raft ensures data consistency across all nodes, which guards against data loss or corruption in the event of individual node failures.

Moreover, the leader-based system of Raft can be leveraged to improve the security of the system. For instance, the leader can employ additional security measures, such as log entry validation and rate limiting, to protect against malicious attacks.

However, it is worth noting that Raft, like any other consensus protocol, is not invulnerable to all types of attacks. For instance, it is susceptible to Sybil attacks, where an attacker creates many fake identities to gain undue influence over the system. Therefore, additional security measures, such as identity verification and admission control, should be implemented alongside Raft to bolster the security of the distributed storage system.

2.2.4 Multi-dimensional Directed Acyclic Graph (DAG)

The multi-dimensional DAG at the heart of the Knit Protocol's architecture plays a critical role in security. By utilizing a DAG, the protocol ensures that there are no cycles within the network that could result in confusion or conflict of information. The multi-dimensionality further enables parallel processing of tasks, thus improving the efficiency of the system and reducing potential points of attack. This inherent structure also aids in achieving global ordering of events, which is fundamental for data consistency and aids in preventing double-spending attacks.

2.2.5 Advanced Encryption Standard (AES)

In addition to the DAG structure and Raft consensus, the Knit Protocol employs AES encryption, a globally recognized encryption standard. This ensures that the data is encrypted during transmission, protecting it from unauthorized access or interception. AES provides a high level of security and is resistant to all known practical attacks when used correctly.

By incorporating these mechanisms, the Knit Protocol provides a secure, robust infrastructure for decentralized ML applications. It effectively addresses the challenges associated with distributed computing systems, thereby instilling trust and reliability among its users.

2.3 Distributed Compute Units

The use of distributed computing is a cornerstone of the Knit Protocol's computational infrastructure. In this system, computational tasks are divided into smaller subtasks which are processed independently across multiple nodes in the network. This setup is particularly beneficial for ML workloads that are typically computation-heavy and benefit greatly from parallelized processing.

Further, distributed computation enhances the system's reliability. As each node operates independently, a failure in one does not affect the entire system. Even in the face of individual node failures, the system maintains its capability to perform computations, thereby increasing fault tolerance.

2.4 Load Balancing

Load balancing is a critical feature of the Knit Protocol, allowing the system to distribute tasks evenly and efficiently across a network of nodes, including individual devices connected via the browser in a peer-to-peer (P2P) system. This approach not only ensures optimal utilization of each node but also effectively manages the global redundancy of tasks.

In the Knit Protocol, load balancing goes beyond traditional models to accommodate the unique characteristics of a P2P system. Here, each participating device is treated as a node capable of processing tasks. Given the variability in the computational capacities of devices, from powerful servers to personal computers and even mobile devices, the protocol employs sophisticated algorithms to assign tasks appropriately.

The Knit Protocol uses a dynamic load balancing strategy, where tasks are not just distributed based on the current workload of a node, but also taking into account other factors such as network latency, computational capabilities, and energy efficiency of the device. The protocol keeps track of the 'health' and capacity of each node and adjusts the task allocation in real-time to ensure smooth and efficient operation of the network.

Furthermore, the Knit Protocol is designed to handle redundancy smartly. In the context of distributed computing, redundancy is often necessary to ensure data integrity and system resilience. However, it is vital to manage redundancy efficiently to prevent unnecessary duplication of tasks. The Knit Protocol achieves this by implementing algorithms that track the state of tasks across the network, preventing unnecessary replication and ensuring that each job is executed by the most suitable node.

By employing such an intelligent load balancing approach, the Knit Protocol can distribute computational tasks effectively across a diverse and globally distributed network. It ensures that each node, regardless of its computational power, can contribute meaningfully to the overall system performance. This system not only maximizes the utilization of available resources but also contributes to a more resilient and efficient network.

3. Probabilistic and Deterministic Verification

Verification is an essential aspect of maintaining the integrity and accuracy of computations performed within the Knit Protocol. Given the distributed nature of the protocol, a robust system is needed to ensure that all computations are performed accurately and reliably. For this purpose, the Knit Protocol incorporates a dual verification system: probabilistic and deterministic.

3.1 Probabilistic Verification

Probabilistic verification involves the use of statistical methods and probabilistic models to estimate the correctness of computations. It's a process wherein the computation performed by a node is verified using a random sampling of data or operations to estimate the overall accuracy.

The primary advantage of probabilistic verification is its computational efficiency. It significantly reduces the need for exhaustive verification processes that would require re-computation of entire tasks, thereby saving computational resources. It offers a reliable approximation of computation accuracy, especially in situations where deterministic verification may be computationally expensive or impractical. Probabilistic verification is particularly applicable for machine learning tasks where the results can be probabilistic in nature.

3.2 Deterministic Verification

On the other hand, deterministic verification is an exact and thorough process wherein the accuracy of computation is conclusively determined. In deterministic verification, the outputs of a computation are verified by cross-verifying with other nodes or by re-computation. It guarantees the correctness of computations by corroborating them against reliable and independently verifiable standards.

Although deterministic verification can be more resource-intensive, it provides a higher degree of certainty compared to probabilistic verification. It is particularly applicable to computations where there's a clear and definitive correct output, and the cost of errors is high.

In the Knit Protocol, the combination of probabilistic and deterministic verification ensures a balance between computational efficiency and integrity. By applying probabilistic verification to reduce the number of computations requiring deterministic verification, the protocol maintains high computational integrity while conserving resources.

4. Scalability and Cost-Efficiency

One of the core design principles of the Knit Protocol is to provide a scalable and cost-efficient platform for distributed computation and storage, especially focused on machine learning tasks. This is achieved through the decentralization of resources, an efficient load balancing mechanism, and a competitive marketplace for compute power.

4.1 Scalability

Scalability, in the context of the Knit Protocol, refers to the network’s ability to accommodate an increasing number of tasks, data, and users without a decline in performance1. This is achieved by utilizing a decentralized architecture built on the DAG (Directed Acyclic Graph) model, which enables a highly scalable system capable of managing a large volume of concurrent tasks. By removing the centralized overheads on scaling, the protocol allows for near-infinite scalability, limited only by worldwide physical hardware limits.

4.2 Cost-Efficiency

The Knit Protocol aims to offer significant cost savings to its users. The protocol establishes an open marketplace where the price of compute resources is determined by supply and demand dynamics. This open marketplace approach enables the unit cost of machine learning computation to reach its fair market equilibrium, leading to a significant reduction in costs compared to traditional centralized providers.

Another crucial aspect contributing to the protocol's cost-efficiency is its utilization of idle resources. By tapping into latent computing power that would otherwise remain unused, the protocol can offer computation at a much lower cost. The protocol is especially beneficial for owners of powerful GPUs, which can generate returns on their hardware by participating in the network.

4.3 Knit Protocol Cost Analysis

To illustrate the cost-efficiency of the Knit Protocol, we provide a comparison of estimated costs with other computation providers (theoretical and actual). The projected cost for using the Knit Protocol is significantly lower due to the efficiencies gained through decentralization and the marketplace model.

Table 1: Provider cost comparison for ML training work (V100-equivalent)

Provider	Approximate hourly cost	Scalability
Ethereum	$15,700	Low
Truebit (+ Ethereum)	$12	Low
GCP on-demand	$2.50	MediumT
AWS on-demand	$2	Medium
Golem Network	$1.20	Low
Vast.ai	$1.10	Low
AWS spot instances	$0.90	Medium
GCP spot instances	$0.75	Medium
Knit Protocol (projected)	$0.40	High

5. Decentralization and Governance

Decentralization and effective governance are vital components of the Knit Protocol, ensuring fair distribution of rewards, promoting community involvement, and fostering an open and sustainable ecosystem.

5.1 Decentralization

The Knit Protocol is a fully decentralized network, both in terms of its computational resources and governance structure. The computational resources are provided by a network of nodes that are distributed globally. Each node in the network contributes computational power and storage capacity, which collectively form the compute infrastructure of the Knit Protocol. This decentralized model offers numerous benefits, including robustness against failures, increased privacy and security, and the potential to utilize a vast amount of idle resources globally.

5.2 Governance

Knit Protocol's governance is designed to be democratic, with decisions driven by the community of token holders. The protocol is initially managed by Knit Limited, the entity responsible for developing the protocol, forming the team, and managing the intellectual property prior to open source launch. Knit Limited operates as a fully remote company, hiring talent from across the globe.

Post the Token Generation Event (TGE), the governance will transition to a decentralized model, where an elected council makes decisions based on on-chain proposals and referenda. Initially, the council will comprise core members of Knit Limited and the early community to expedite protocol development. Over time, the council will become more decentralized, allowing wider community participation.

5.3 Token Model and Treasury

The Knit Protocol issues tokens during the TGE, controlled by the Knit Foundation, which will serve as the representation of stakeholders in the network. The tokens are used to incentivise network participation and for governance purposes, including voting on proposals related to protocol upgrades and changes.

The Knit Foundation controls a treasury, primarily funded by a small percentage of each task fee. The treasury funds are utilized to further the aims of the protocol, including funding the continued development of the protocol and the broader ecosystem.

5.4 Future Governance Development

As the Knit Protocol evolves, so will its governance structure. Over time, the governance model will incorporate additional mechanisms to ensure equitable distribution of power and to enable effective decision-making. Further research will be conducted into governance models that can handle the unique challenges posed by a decentralized machine learning ecosystem.

6. Federated Learning on the Knit Protocol

Federated Learning (FL) is an advanced machine learning approach that allows model training to be distributed across multiple devices or servers, each holding local data samples, without having to share the raw data. This section explains how the Knit Protocol facilitates federated learning, while overcoming the challenges associated with distributed learning.

6.1 The Role of the Knit Protocol in Federated Learning

FL, being a decentralized learning approach, naturally aligns with the decentralized architecture of the Knit Protocol. Each node in the Knit network may act as a participant in the FL process, training local models on their specific data, and then sharing the model parameters (not the data) with a central server or aggregator for global model updates.

The aggregator, in the context of the Knit Protocol, is also decentralized. The role of the aggregator may rotate among nodes or could be performed by multiple nodes in a distributed fashion. The protocol's load balancing feature can assist in choosing an aggregator that will optimize the overall system performance.

The task execution layer of the Knit Protocol ensures the correct execution of the FL task using the proof-of-computation mechanisms. This makes the FL process on the Knit Protocol reliable, verifiable, and resistant to adversarial attacks.

6.2 Privacy and Security in Federated Learning

One of the key advantages of FL is the preservation of privacy, as raw data never leaves the local device. However, sharing model parameters can still leak sensitive information. The Knit Protocol incorporates several strategies to ensure privacy and security in FL.

Differential privacy techniques can be implemented in the model update phase, ensuring that the shared updates do not reveal sensitive information about the local data. Secure Multi-Party Computation (SMPC) can be used when aggregating model parameters to prevent any node from having access to the complete set of model updates.

6.3 Resource Management and Efficiency in Federated Learning

The Knit Protocol optimizes resource management and efficiency in FL through its resource scheduling and load balancing mechanisms. Given the heterogeneous and dynamic nature of the Knit network, some nodes may have more powerful hardware or may be less occupied than others. These nodes can be given priority in the model training phase to accelerate the learning process. The load balancing mechanism can distribute the FL tasks in a way that minimizes the overall system load.

6.4 Federated Learning Applications on the Knit Protocol

The Knit Protocol is suitable for a wide range of FL applications, including those in healthcare, finance, telecommunications, and more. Due to the protocol's focus on privacy and security, it can be especially beneficial for applications where data privacy is paramount. Its efficiency in managing resources makes it a practical solution for large-scale FL tasks.

7. Future Directions

As a pioneering platform for decentralized machine learning, the Knit Protocol is positioned at the forefront of innovation. However, in a rapidly evolving technological landscape, it is crucial to look ahead and anticipate future trends and requirements. Here are some future directions for the Knit Protocol.

7.1 Enhanced Verification Mechanisms

While the current probabilistic and deterministic verification mechanisms are robust, there is room for exploration of new verification techniques. Techniques such as interactive proof systems, zero-knowledge proofs, and advancements in homomorphic encryption can be considered to enhance the proof-of-computation processes.

7.2 Blockchain Interoperability

Blockchain interoperability is a pressing need in the blockchain community. It allows different blockchains to interact and transact with each other, breaking the existing silos. The Knit Protocol, being built on a specific blockchain platform, could potentially support interoperability with other blockchains in the future. This would enable the protocol to leverage the capabilities of different blockchains and increase the number of potential nodes.

7.3 Advanced Privacy-Preserving Techniques

With privacy being a fundamental requirement for the Knit Protocol, especially for Federated Learning applications, it's necessary to continue researching and implementing advanced privacy-preserving techniques. These could include secure multi-party computation, fully homomorphic encryption, and federated analytics.

7.4 Broader Machine Learning Support

While the Knit Protocol currently focuses on Federated Learning, future versions of the protocol could support other machine learning paradigms, such as reinforcement learning, online learning, and ensemble learning.

8. Conclusion

The Knit Protocol presents a novel approach to decentralized machine learning computation and storage. By leveraging the strengths of blockchain technology and the principles of federated learning, the Knit Protocol has developed a system that is scalable, cost-efficient, and robust in its execution of machine learning tasks.

The underlying Directed Acyclic Graph structure of the protocol, combined with its sophisticated load balancing mechanisms, enables the network to handle an extensive volume of concurrent tasks efficiently. It offers a solution to the rising costs of machine learning computation by creating an open marketplace for compute resources and decentralized storage.

Its design inherently supports federated learning, providing a solution that respects user privacy and security. The protocol's governance is also designed to be democratic, promoting community participation and ensuring the equitable distribution of rewards.

As the field of decentralized machine learning continues to grow, the Knit Protocol is well-positioned to become a leading platform for developers and researchers alike. Its focus on scalability, cost-efficiency, security, and privacy sets the Knit Protocol apart, making it a promising contender in the race to decentralize the future of machine learning.