Federated learning experiments
We run federated learning experiments — an approach where a model is trained on data that does not leave devices/sources, without centralized collection. It is a promising privacy technology for rare, specific tasks. Honestly upfront: this is an experimental, niche, bleeding-edge direction — for the vast majority of sites it is overkill and impractical; we will honestly say if you do not need it.
Federated learning experiments — overview
Federated learning experiments is a research/experimental project: we work through a hypothesis and a pilot where models are trained on distributed data without its centralized collection (data stays on devices/in sources, only model updates are exchanged). Honestly about maturity and applicability, this is key: federated learning is a BLEEDING-EDGE technology, complex, resource-intensive and justified only in rare specific cases (strict privacy requirements with a large distributed data volume, e.g. large multi-organization or on-device scenarios); for a regular site/online store it is almost always OVERKILL and impractical — regular analytics/ML or privacy setup is simpler and more effective. We will honestly assess and most likely advise against it if it is not your case. Honestly about the result: this is an experiment with an UNCERTAIN outcome and no guarantee of practical benefit or metric growth — the pilot's goal is to test feasibility and sense, not to get a guaranteed result; a 'not worthwhile' conclusion is possible, and it is a valid outcome. Honestly about requirements: serious engineering, distributed data/devices, expertise and budget are needed; the infrastructure and its cost — separate. Honestly about the essence: this is an R&D pilot, NOT a ready turnkey production solution and NOT a guarantee. An important boundary: this is federated learning, not regular AI personalization (659 — for practical tasks), not ML analytics and not privacy setup (681). If you have no specific task where centralized data collection is impossible, you most likely do not need this direction — we will say so directly and honestly. Picture this: not 'trendy AI on the site' but a sober check — whether you have a real task for this specific approach and whether it is worth the effort. The base price starts from 40,000 ₽ per pilot/study; it strongly depends on the task and infrastructure (separate).
Problems we solve
- Strict privacy requirements prevent centralized data collection.
- Distributed data/devices that cannot be brought into one place.
- You need to assess whether federated learning applies to your task.
- You do not want 'AI for hype' — you need a sober feasibility check.
What's included in the Federated learning experiments service
- Working through the hypothesis and assessing applicability (often — 'not needed')
- Designing a federated learning pilot for a specific task
- Assessing requirements: data, devices, infrastructure, budget
- A pilot testing feasibility and sense
- An honest conclusion (including 'not worthwhile')
- Comparison with simpler alternatives (regular ML/privacy setup)
- A report with limitations and recommendations
- Reviewing with you
What you get
- A sober assessment of whether you need this approach (often — no)
- Understanding of feasibility and requirements
- A pilot with an honest conclusion (no benefit guarantee)
- Comparison with simpler and cheaper alternatives
How the work goes: steps
- We assess the task: is there a real need (if not — honestly advise against)
- We design and run a pilot when the need is justified
- We compile an honest conclusion and comparison with alternatives, review with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Do I need federated learning on my site?
Almost certainly not. It is a niche bleeding-edge technology for rare tasks with strict privacy and distributed data. For a regular site/store it is overkill — regular analytics/ML or privacy setup is simpler. We will honestly advise against it if it is not your case.
Will it give metric growth?
No guarantee. It is an experiment with an uncertain outcome; the goal is to test feasibility and sense, not to get a guaranteed result or growth. An honest 'not worthwhile' conclusion is possible — a valid pilot outcome.
Why so expensive and uncertain?
Federated learning is a complex R&D direction: it needs engineering, distributed data/devices, expertise and infrastructure (separate). This is a pilot/study, not a ready turnkey solution; uncertainty is part of honest research.
About the provider
The «Federated learning experiments» service is provided by PDV Expert — a team specialising in «Conversion & analytics». We work under contract and deliver a written report with recommendations.