FLock.io has designed a decentralized architecture that encourages diverse participants to actively contribute to the operation of a federated learning platform while effectively mitigating attack vectors stemming from the vulnerabilities of traditional federated learning.
However, if there are significant entry barriers for local devices to directly contribute to model generation, the performance and diversity of democratized models created through FLock.io may face limitations.
To address this, FLock.io aims to consistently realize and strengthen its vision of democratizing the entire lifecycle of AI model creation by taking initiatives to more practically integrate edge computing concepts into its existing architecture.
With the rapid advancement of the internet and IT technologies, the explosion of content and information has led people to seek highly personalized services tailored specifically to their unique needs and preferences. Against this backdrop, the demand for personalized experiences has surged, evolving from a focus on mere efficiency to a growing desire for services that align with individual identity and context.
At the heart of this transformation lie big data and AI technologies. Companies are leveraging AI to collect and analyze consumer data, designing tailored services that go beyond convenience to foster deeper emotional connections by understanding consumer contexts. Across various domains closely integrated into everyday life, such as social media, e-commerce platforms, and streaming services, AI increasingly reflects individual preferences and tastes, optimizing consumer experiences and offering highly personalized interactions.
However, as AI is expected to take on more significant roles, concerns regarding data ethics, privacy, and excessive reliance on centralized services have intensified. If the advancement of AI technologies fails to adequately address issues of cost efficiency and trust, there are growing apprehensions that the widespread adoption of AI across industries may face significant hurdles.
Ultimately, for AI services to achieve greater sophistication, there is a need for a structural shift toward democratic learning models that ensure data rights and move away from centralized approaches.
The recent proposal by FLock.io to combine edge computing and decentralized federated learning (DeFL) presents a fascinating opportunity. This approach not only protects the exclusivity of personal data but also lowers the barriers to AI model training, enabling the scalable development of diverse AI services.
Source: FLock.io : The Base Layer for AI Democratization
First of all, federated learning involves sending AI base models to local clients, each of which possesses unique datasets. These clients train the models locally, and the trained parameters are then aggregated by a central server to create a global model. This global model is subsequently redistributed to local clients, iteratively improving and refining it until a final model is achieved.
In this system, each participant (e.g., hospitals, companies, individuals) retains their data locally while contributing to model training. Only the training outcomes (parameters) are shared with the central server, ensuring the original data remains protected from exposure to third parties. Moreover, as the model structure and parameters are transmitted from the central server, data contributors can transparently verify how their data is being utilized and, consequently, trust the final model.
Source: FLock.io : The Base Layer for AI Democratization
However, traditional federated learning faces certain limitations despite its advantages. These include:
The difficulty of recruiting a sufficiently large and honest participant base for effective model training.
A continued reliance on centralized operations for some aspects of model training.
To address these challenges, FLock.io has developed a blockchain-based system that incorporates a transparent and verifiable network structure alongside an incentive mechanism. This design encourages diverse participants to actively contribute to the federated learning platform while effectively mitigating various attack vectors arising from the aforementioned limitations.
While FLock.io's decentralized federated learning (DeFL) approach holds the potential to enhance the usability of personal data (e.g., local data) and encourage participation from diverse contributors to enable more robust global model training, its success depends on overcoming barriers to participation. Without lowering these entry barriers, the performance and diversity of the democratized models developed through FLock.io may fall short of expectations.
In other words, even if the environment is open to everyone, practical participation requires simplifying and streamlining the model training process to make it more accessible and user-friendly.
To address this, FLock.io leverages the concept of edge computing to reduce entry barriers for participants, enhancing the infrastructure and interfaces needed for various local devices to easily engage in model training - edge computing refers to the storage, processing, and analysis of data near its source of generation, enabling near real-time results.
By integrating edge computing initiatives, FLock.io's model training architecture is expected to:
Significantly enhance the performance and intelligence of localized, domain-specific applications,
Reduce bandwidth requirements, and
Utilize idle computing resources on edge devices, alleviating the computational burden on central servers and delivering faster real-time insights.
In summary, while decentralized federated learning is structurally designed to encourage participation from a diverse range of contributors and ensure a sustainable AI model lifecycle, edge computing initiatives serve as a catalyst to facilitate the more seamless operation of this decentralized framework.
Although no detailed initiatives have been announced yet, FLock.io has disclosed plans to strengthen its edge computing initiatives by lowering the barriers to AI model training starting with cases with Apple Silicon devices and eventually expanding to include a broader range of personal computers and mobile devices.
As outlined in my previous article, the vision and design of FLock.io reflect a strong commitment to democratizing the entire lifecycle of AI services—from their initial creation to practical deployment.
Firstly, FLock.io democratizes the model creation process by enabling individuals and organizations, regardless of their expertise in AI, to easily propose models. Moreover, by allowing access to personal and proprietary datasets from data providers during the model training process, FLock.io facilitates the democratization of data.
Additionally, as the application of individual models expands, the platform ensures that the economic and social value generated is widely shared among diverse participants and contributors. It enables anyone to benefit from the network's progress and participate in its operation, thus paving the way for the democratization of value distribution in tandem with AI's advancement.
And now, by materializing the concept of edge computing—capable of providing more contextual and in-depth insights while lowering barriers to learning—and applying it to existing architectures, FLock.io takes a significant step toward the democratization of participation in model training.
The advancement of technology in ways that allow it to scale across diverse industries necessitates experimentation and research led by decentralized, diverse entities. FLock.io’s efforts to democratize AI model training not only inspire the pursuit of AI democratization but also provide critical insights and lessons for blockchain-based services as they work to expand and enhance their unique approaches.
Related Articles, News, Tweets etc. :
FLock.io - Twitter Posting
FLock.io - Whitepaper
Four Pillars - FLock.io : The Base Layer for AI Democratization
FSP Group - What Is Edge Computing? 8 Examples and Architecture You Should Know
Ádám Szaller, Christian Fries, Botond Kádár - ”Financial aspects of a trust-based resource sharing platform,” CIRP Journal of Manufacturing Science and Technology, Volume 43, 2023, Pages 88-105.
Related People, Projects :
Four Pillars (@FourPillarsFP) - Jay (@JayLovesPotato)
FLock.io (@flock_io)
Solana
