Federated Learning Without Central Trust
Replace your central FedAvg server with MPC-based aggregation—no single party sees model updates.
Coming soon
Federated Learning Without Central Trust
Replace your central FedAvg server with MPC-based aggregation—no single party sees model updates.
Coming soon
Federated Learning Without Central Trust
Replace your central FedAvg server with MPC-based aggregation—no single party sees model updates.
Coming soon
Who's it for?
Teams using Flower (Python) or similar FL frameworks who want a decentralized, “no central trust” aggregator.
Who's it for?
Teams using Flower (Python) or similar FL frameworks who want a decentralized, “no central trust” aggregator.
Who's it for?
Teams using Flower (Python) or similar FL frameworks who want a decentralized, “no central trust” aggregator.
No central trust
No central trust
No central trust
Answers-only outputs
Only global weights/metrics
Answers-only outputs
Only global weights/metrics
Answers-only outputs
Only global weights/metrics
Minimal change to training code & ops
Minimal change to training code & ops
Minimal change to training code & ops
What you get
FedAvg Lang module (drop-in for rounds)
VM orchestration for round management
App-specific SDK (Python/Flower adapter) to call the aggregator like today
Your Integration Perks
Keep your Flower pipeline
Install the adapter SDK
Point your aggregator endpoint to Stoffel
What you get
FedAvg Lang module (drop-in for rounds)
VM orchestration for round management
App-specific SDK (Python/Flower adapter) to call the aggregator like today
Your Integration Perks
Keep your Flower pipeline
Install the adapter SDK
Point your aggregator endpoint to Stoffel
What you get
FedAvg Lang module (drop-in for rounds)
VM orchestration for round management
App-specific SDK (Python/Flower adapter) to call the aggregator like today
Your Integration Perks
Keep your Flower pipeline
Install the adapter SDK
Point your aggregator endpoint to Stoffel
How it Works
Each party computes local updates as usual.
FedAvg in Stoffel Lang runs via Stoffel VM to combine updates—no peer sees another’s.
Only the new global model (and approved metrics) is shared.
Data never leaves your environment
Team A train locally
Team B train locally
Team C train locally
Stoffel combines updates
Everyone gets a better model
How it Works
Each party computes local updates as usual.
FedAvg in Stoffel Lang runs via Stoffel VM to combine updates—no peer sees another’s.
Only the new global model (and approved metrics) is shared.
Data never leaves your environment
Team A train locally
Team B train locally
Team C train locally
Stoffel combines updates
Everyone gets a better model
How it Works
Each party computes local updates as usual.
FedAvg in Stoffel Lang runs via Stoffel VM to combine updates—no peer sees another’s.
Only the new global model (and approved metrics) is shared.
Data never leaves your environment
Team A train locally
Team B train locally
Team C train locally
Stoffel combines updates
Everyone gets a better model
Common Questions
Is there any data movement?
How are stragglers handled?
DP/clipping?
Common Questions
Is there any data movement?
How are stragglers handled?
DP/clipping?
Common Questions
Is there any data movement?
How are stragglers handled?
DP/clipping?
Products
© 2025 Stoffel. All rights reserved.
Products
© 2025 Stoffel. All rights reserved.
Products
© 2025 Stoffel. All rights reserved.