Notes form the 7/14/2022 meeting of TFF collaborators

Participants: Krzysztof Ostrowski (Google), Boyi Chen (LinkedIn)

Boyi’s update on LinkedIn’s progress and plans.

• Onboarded to TFF and integrated into ML infra
• Doing offline experiments on use of TFF for enterprise solutions
• Three areas of interest
  • Freerider attacks
    • Someone wants to contribute zeros, reap benefits
    • Two goals - detection, solutions
    • Model poisoning a distinct goal, but seemingly related
  • Bias with heavily skewed contributors
    • Some contributors having much more data than others
    • Goes both ways - heavy users over-influencing the model, but also lots of lightweight users dragging performance down
  • Cross-silo FL for a mixture of data from LinkedIn and from outside
    • Guarantees on data not mixing
  • Simulations of on-device FL
    • Simulation capability already exists - we’re talking about simulating the behaviors seen in a realistic prodution environment
    • Vary distributions of things like device processing power to asses how it may impact training performance
• Currently not much progress running on Azure, so punt on this for now

Modes of contributing / working together:

• Algorithms and coimponents in TFF for detecting freeriders and mitigating that
  • Design doc - loop in people from both ends to help improve
  • LinkedIn could contribute code
  • Tentatively LinkedIn to own or co-own a directory within TFF repo where this could go - tbd whether one or more of these and where they would go

TFF’s plans

• Empower partners to build platforms based on TFF
  • Components
  • References architectures
  • Both cross-silo and cross-device
    • Some code is already in OSS, more code upcoming
  • End-to-end privacy, etc., guarantees for platform partners

Next steps:

• Create individual proposals to iterate on with people from both sides
• Prioritize together
  • Maybe that means increasing frequence to once per 2 weeks
  • Pick topics to unpack, loop in people interested in the topic