JAM: Joint Allocation and Matching for Sample Sharing under Emerging Environments

In edge deployments without cloud connectivity, clients encounter different new environments at different times, such as distribution shifts caused by lighting or terrain. Directing information about environments already encountered by some clients to those that have not yet encountered them can support early adaptation. With limited communication, however, each sender must decide whom to send to, how much to send, and what to send. Two challenges arise: class labels do not readily reveal environmental differences within a class, and the same sample has different value to different clients. We propose JAM (Joint Allocation and Matching), which measures differences using features from the common initial model rather than class labels and jointly decides whom to send to, how much to send, and what to send within the budget. Multi-seed experiments on three datasets show that, compared with random sharing, separate allocation and selection, sample selection, and prototype exchange, JAM can significantly improve deployment performance in new environments on datasets with identifiable within-class environmental deviations, without sacrificing average old-environment loss. Quantizing feature averages in JAM control metadata to 4-bit reduces communication while retaining the main adaptation gains.