3 research outputs found
FLIPS: Federated Learning using Intelligent Participant Selection
This paper presents the design and implementation of FLIPS, a middleware
system to manage data and participant heterogeneity in federated learning (FL)
training workloads. In particular, we examine the benefits of label
distribution clustering on participant selection in federated learning. FLIPS
clusters parties involved in an FL training job based on the label distribution
of their data apriori, and during FL training, ensures that each cluster is
equitably represented in the participants selected. FLIPS can support the most
common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To
manage platform heterogeneity and dynamic resource availability, FLIPS
incorporates a straggler management mechanism to handle changing capacities in
distributed, smart community applications. Privacy of label distributions,
clustering and participant selection is ensured through a trusted execution
environment (TEE). Our comprehensive empirical evaluation compares FLIPS with
random participant selection, as well as two other "smart" selection mechanisms
- Oort and gradient clustering using two real-world datasets, two different
non-IID distributions and three common FL algorithms (FedYogi, FedProx and
FedAvg). We demonstrate that FLIPS significantly improves convergence,
achieving higher accuracy by 17 - 20 % with 20 - 60 % lower communication
costs, and these benefits endure in the presence of straggler participants