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