Adaptive Data Admission and Retention for Streaming Federated Learning
2026-07-27 • Machine Learning
Machine LearningArtificial IntelligenceNetworking and Internet Architecture
AI summaryⓘ
The authors study a type of machine learning where many devices (clients) learn together but have limited memory and varying costs to collect new data. They create a system where the server decides which data to accept and clients manage which data to keep, aiming to minimize learning errors while respecting memory and cost limits. They design a method (ACDPP) that smartly balances keeping useful data and controlling costs, proving it performs nearly as well as an ideal system without limits. Experiments show their method works well in practice under these constraints.
Federated LearningStreaming DataMemory ManagementSampling CostExcess RiskDrift-Plus-PenaltySublinear RegretBuffer ConstraintsOnline AdmissionRetention Rule
Authors
Zhuoyi Zhao, Ben Liang
Abstract
We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time. We consider a joint server-side admission and client-side memory-management framework with the objective of minimizing the cumulative excess population risk under a sampling-cost budget and buffer constraints. We first derive a learning-error bound that explicitly captures the effects of instantaneous training sample size, distinct-sample growth, and reuse imbalance through a characterization of the effective sample size. Through a surrogate penalty obtained from this bound, we develop an Active-Constraint Drift-Plus-Penalty (ACDPP) policy that combines a structured client-side $K$-step retention rule with a server-side online admission rule and a time-varying rectangular admission region. We further present a sequence of comparison arguments, via an auxiliary constant-admission policy, that connects the ACDPP learning bound to a costless oracle benchmark. This yields explicit guarantees in terms of sublinear regret and sampling-cost violation, while the buffer-occupancy violation is controlled through offline selection of the retention horizon. Experiments on multiple datasets demonstrate that the proposed policy remains close to the oracle benchmark while satisfying the sampling-cost and buffer constraints.