Multi-Objective Federated Optimization for Decentralized AI-Driven Computing Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I1P101Keywords:
Federated Learning, Multi-Objective Optimization, Pareto Front, Differential Privacy, Secure Aggregation, Fairness In Ai, Energy-Aware Learning, Communication Efficiency, Non-Iid Data, Personalized Fl, Robustness To Adversaries, Reinforcement Learning SchedulerAbstract
Decentralized AI deployments must optimize beyond raw accuracy to meet real-world constraints such as latency, privacy, energy, fairness, and robustness. This paper presents a unified framework for Multi-Objective Federated Optimization (MOFO) that learns Pareto-efficient models under heterogeneous, non-IID data and volatile participation. We formulate cross-device and cross-silo federated learning as a constrained multi-objective program balancing task loss with system- and society-level objectives: end-to-end latency, communication cost, device energy, demographic parity, and adversarial robustness. The framework combines (i) adaptive scalarization with Lagrangian relaxation to enforce hard budgets, (ii) Pareto-front exploration via evolutionary search and hypervolume-guided updates, and (iii) personalized FL through meta-learning and proximal regularization to respect client drift. To reduce communication while preserving privacy, we integrate sparsified/quantized updates, secure aggregation, and calibrated differential privacy, a bandit/RL client scheduler selects participants by marginal Pareto gain and energy profile. Robustness is improved through gradient clipping, Byzantine-resilient aggregation, and federated knowledge distillation. We propose evaluation protocols and indicators (hypervolume, ε-indicator, fairness gaps, joules/sample, and p95 latency) and demonstrate that MOFO yields diverse Pareto-optimal models enabling operators to trade accuracy for efficiency or fairness without retraining. Ablations show consistent gains over single-objective FL baselines under stragglers, intermittent connectivity, and non-IID shifts. The framework provides a practical path to deploy equitable, resource-aware, and trustworthy decentralized AI
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