Design a federated learning system: system design interview question

How the interviewer opens it

“Design a system that trains a shared model from data on user devices without collecting their raw examples. Devices have different capabilities and do not always remain connected.”

The prompt is vague on purpose. The first minutes are for you. Reduce the prompt to the core features and agree on the numbers before you draw.

What to clarify first

Task and users
Round scale
Payloads and read:write mix
Device limits
Latency
Privacy threat model
Privacy across releases
Consistency and availability
Durability and retention
Existing systems and exclusions

The interviewer has the answers. In a SysRound round, Alex gives these answers, as a real interviewer does.

Scope of a strong answer

Core requirements

Select eligible devices and distribute a shared model
Combine device updates without exposing individual updates
Handle device loss and different device capabilities
Release models and metrics within a defined privacy budget

Usually out of scope

Design a new model architecture
Design cryptographic primitives
Defend against compromised devices or a malicious coordinator
Build account and device enrollment systems

What interviewers ask about

Rounds with lost connections: Devices can disappear during several protocol phases. Retries must not count an update twice or apply a round to the wrong model.
Which devices contribute: Fast, connected phones can dominate the model. Their data may differ from data on devices that rarely finish.
What the server can learn: Model updates can reveal examples without containing raw text. Device loss also changes which aggregate the server can safely recover.
Privacy across model versions: Repeated model and metric disclosures consume privacy budget. Overlapping time windows and repeated device contributions make a single-round guarantee insufficient.

Common mistakes

Treats the absence of raw examples as a complete privacy guarantee.
Uses unlimited local work despite device limits and unequal data volumes.
Combines updates from different model versions without defining the algorithm.
Requires every invited device to finish a round.
Claims random invitations remove all participation bias.
Ignores cumulative privacy cost or treats rolling windows as calendar months.

The bar by level

Senior (L5/E5): Defines recoverable rounds and version rules. Explains secure aggregation, contribution bounds, rolling privacy accounting, and device bias. Uses payload estimates to choose storage and delivery.
Staff (L6+/E6+): Defines the trust boundary and disclosure rules precisely. Tests privacy assumptions against selection, overlapping windows, retries, metrics, and rollback. Balances model quality, device access, and enforceable release limits.

Sources · 1 reference

These records explain where the question appears. A company tag or candidate report does not guarantee a future interview question.

SysRound · 1 record

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Design a federated learning system
Reported date not specified
Source snapshot: Nov 12, 2026
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How the interviewer opens it

“Design a system that trains a shared model from data on user devices without collecting their raw examples. Devices have different capabilities and do not always remain connected.”

The prompt is vague on purpose. The first minutes are for you. Reduce the prompt to the core features and agree on the numbers before you draw.

What to clarify first

Task and users

Round scale

Payloads and read:write mix

Device limits

Latency

Privacy threat model

Privacy across releases

Consistency and availability

Durability and retention

Existing systems and exclusions

The interviewer has the answers. In a SysRound round, Alex gives these answers, as a real interviewer does.

Scope of a strong answer

Core requirements

Select eligible devices and distribute a shared model
Combine device updates without exposing individual updates
Handle device loss and different device capabilities
Release models and metrics within a defined privacy budget

Usually out of scope

Design a new model architecture
Design cryptographic primitives
Defend against compromised devices or a malicious coordinator
Build account and device enrollment systems

What interviewers ask about

Rounds with lost connections

Devices can disappear during several protocol phases. Retries must not count an update twice or apply a round to the wrong model.

Which devices contribute

Fast, connected phones can dominate the model. Their data may differ from data on devices that rarely finish.

What the server can learn

Model updates can reveal examples without containing raw text. Device loss also changes which aggregate the server can safely recover.

Privacy across model versions

Repeated model and metric disclosures consume privacy budget. Overlapping time windows and repeated device contributions make a single-round guarantee insufficient.

Common mistakes

Treats the absence of raw examples as a complete privacy guarantee.

Uses unlimited local work despite device limits and unequal data volumes.

Combines updates from different model versions without defining the algorithm.

Requires every invited device to finish a round.

Claims random invitations remove all participation bias.

Ignores cumulative privacy cost or treats rolling windows as calendar months.

The bar by level

Senior (L5/E5)

Defines recoverable rounds and version rules. Explains secure aggregation, contribution bounds, rolling privacy accounting, and device bias. Uses payload estimates to choose storage and delivery.

Staff (L6+/E6+)

Defines the trust boundary and disclosure rules precisely. Tests privacy assumptions against selection, overlapping windows, retries, metrics, and rollback. Balances model quality, device access, and enforceable release limits.