Dynamic D2D-Assisted Federated Learning over O-RAN: Performance
  Analysis, MAC Scheduler, and Asymmetric User Selection
Payam Abdisarabshali Kwang Taik Kim Michael Langberg Weifeng Su Seyyedali Hosseinalipour
Existing studies on federated learning (FL) are mostly focused on system
orchestration for static snapshots of the network and making static control
decisions (e.g., spectrum allocation). However, real-world wireless networks
are susceptible to temporal variations of wireless channel capacity and users'
datasets. In this paper, we incorporate multi-granular system dynamics (MSDs)
into FL, including (M1) dynamic wireless channel capacity, captured by a set of
discrete-time events, called $\mathscr{D}$-Events, and (M2) dynamic datasets of
users. The latter is characterized by (M2-a) modeling the dynamics of user's
dataset size via an ordinary differential equation and (M2-b) introducing
dynamic model drift}, formulated via a partial differential inequality} drawing
concrete analytical connections between the dynamics of users' datasets and FL
accuracy. We then conduct FL orchestration under MSDs by introducing dynamic
cooperative FL with dedicated MAC schedulers (DCLM), exploiting the unique
features of open radio access network (O-RAN). DCLM proposes (i) a hierarchical
device-to-device (D2D)-assisted model training, (ii) dynamic control decisions
through dedicated O-RAN MAC schedulers, and (iii) asymmetric user selection. We
provide extensive theoretical analysis to study the convergence of DCLM. We
then optimize the degrees of freedom (e.g., user selection and spectrum
allocation) in DCLM through a highly non-convex optimization problem. We
develop a systematic approach to obtain the solution for this problem, opening
the door to solving a broad variety of network-aware FL optimization problems.
We show the efficiency of DCLM via numerical simulations and provide a series
of future directions.