Distributed Dynamic Associative Memory via Online Convex Optimization

2026-07-09 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

A new concept, Distributed Dynamic Associative Memory (DDAM), and its accompanying algorithm, DDAM-TOGD, address the limitations of traditional centralized and static associative memory (AM) in multi-agent, time-varying data environments. DDAM-TOGD is a novel tree-based distributed online gradient descent algorithm enabling multiple agents to maintain local AMs, storing their own associations while selectively memorizing information from other agents based on a specified interest matrix. The algorithm updates memory on the fly via inter-agent communication over designated routing trees. Rigorous theoretical analysis proves sublinear static regret in stationary environments and a path-length dependent dynamic regret bound in non-stationary settings, highlighting the impact of communication delays and network structure. A combinatorial tree design strategy, DDAM-TOGD*, optimizes routing to minimize these delays. Numerical experiments on a synthetic dataset using the DeltaNet model and a real wireless traffic dataset from 16 high-usage access points demonstrate DDAM-TOGD's superior accuracy and robustness compared to baselines like consensus-based distributed optimization, particularly in heterogeneous and personalized scenarios where C-DOGD's regret plateaus.

Key takeaway

For AI Architects designing multi-agent systems with streaming data, DDAM-TOGD offers a robust solution for adaptive memory management. You should consider implementing tree-based communication topologies and personalized memory mechanisms to overcome the limitations of centralized or consensus-driven approaches. Optimizing routing trees to minimize communication delays will directly improve system performance and regret bounds, ensuring your agents can adapt effectively to non-stationary environments and heterogeneous data streams.

Key insights

Distributed Dynamic Associative Memory (DDAM) enables multi-agent, adaptive memory recall in dynamic, networked environments via tree-based online gradient descent.

Principles

• Associative memory can be distributed and dynamic.
• Communication delays directly impact learning regret.
• Personalized memory outperforms forced consensus.

Method

DDAM-TOGD uses tree-based online gradient descent. Each agent broadcasts memory parameters along spanning trees, receives gradient feedback, and updates its local AM, optimizing for selective recall from other agents.

In practice

• Optimize routing trees to minimize communication delays.
• Allow agents to specialize their memory mechanisms.
• Selectively incorporate relevant external agent data.

Topics

• Distributed Associative Memory
• Online Convex Optimization
• Multi-Agent Systems
• Regret Analysis
• Communication Networks
• Wireless Traffic Prediction

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.